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a standard encryption method that could be used to protect the data since all data are mostly uploaded to cloud now a days , also cybercriminals constantly developing new ways to break encryption algorithms. However, there are a number of promising areas of research, such as quantum cryptography and post-quantum cryptography. These new encryption algorithms are designed to be resistant to even the most powerful computers, and they could offer a significant improvement in data security
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Dear Scholars, Researchers, and Academics, We are pleased to announce a Call for Papers for the upcoming Special Issue to be hosted by the Mesopotamian Academic Press. This prestigious event is dedicated to fostering intellectual exchange and advancing scholarship in the field of Computer Science. The Mesopotamian Academic Press takes pride in its commitment to nurturing the academic community by providing a platform for thought-provoking discussions and interdisciplinary collaborations. We invite contributions from scholars, researchers, and academics working in various disciplines, such as Big Data, Cybersecurity, Information Technology , and beyond, to submit their original research papers and engage in lively discussions that delve into the multifaceted dimensions. Sincerely, Mesopotamian Academic Press https://mesopotamian.press/journals/index.php/index/index
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Publish your paper for free
_________________________
Dear Researchers and postgraduate students
MESOPOTAMIAN JOURNAL OF BIG DATA (MJBD) issued by Mesopotamian Academic Press, welcomes the original research articles, short papers, long papers, review papers for the publication in the next issue the journal doesn’t requires any publication fee or article processing charge and all papers are published for free
Journal info.
1 -Publication fee: free
2- Frequency: 1 issues per year
3- Subject: computer science, Big data, Parallel Processing, Parallel Computing and any related fields
4- ISSN: 2958-6453
5- Published by: Mesopotamian Academic Press.
Managing Editor: Dr. Ahmed Ali
The journal indexed in
1- Croosref
2- DOAJ
3- Google scholar
4- Research gate
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Writing a paper for my Master's degree. Need an example of cybersecurity attacks by students to the school they were in.
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What strategies can be developed using deep learning to strengthen data encryption and privacy in cloud environments?
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Dear tajinder kumar Saini,
It should be helpful to review information presented below:
Deep learning can be utilized to enhance data encryption and privacy in cloud environments by leveraging its capabilities in pattern recognition, anomaly detection, and generating cryptographic keys. Here are some strategies that combine deep learning with encryption and privacy techniques:
  1. Privacy-Preserving Data Generation:Generative models like Generative Adversarial Networks (GANs) can be used to generate synthetic data that retains statistical properties of the original data. This synthetic data can be used for analysis without exposing sensitive information.
  2. Homomorphic Encryption:Deep learning models can be designed to operate on encrypted data using homomorphic encryption. This enables computation on encrypted data without decrypting it, thereby preserving privacy during computation.
  3. Secure Multi-Party Computation (SMPC):Deep learning models can be distributed across multiple parties using SMPC protocols, allowing them to collaboratively train models without sharing raw data. This protects the privacy of individual data while collectively improving the model.
  4. Anomaly Detection and Intrusion Detection:Deep learning models, such as autoencoders or recurrent neural networks, can be trained to detect anomalous patterns in network traffic or user behavior. This helps identify potential security breaches or unauthorized access.
  5. Data Masking and Tokenization:Deep learning models can be trained to tokenize or mask sensitive data in a way that preserves its utility for analysis while preventing the reconstruction of the original data.
  6. Enhanced Key Generation:Deep learning models can assist in generating more robust encryption keys by analyzing patterns in the data that might lead to weak keys. This can improve the overall security of encryption mechanisms.
  7. Adaptive Encryption:Deep learning can be used to dynamically adjust encryption parameters based on the sensitivity of data or the context in which it's being accessed. This helps tailor encryption strength to specific needs.
  8. Behavior-Based Access Control:Deep learning models can analyze user behavior to establish a baseline for normal access patterns. Deviations from this pattern can trigger additional authentication steps or access restrictions to prevent unauthorized access.
  9. Secure Data Sharing:Encrypted data can be securely shared across organizations or parties using deep learning-based techniques for access control, ensuring that only authorized users can decrypt and access the shared data.
  10. Federated Learning:Deep learning models can be trained using federated learning, allowing devices or entities to collaboratively train a shared model without sharing raw data. This maintains data privacy while improving model performance.
  11. Privacy-Preserving AI Services:Deep learning models can be deployed as services in the cloud that perform computations on encrypted data, enabling users to interact with AI models without revealing their sensitive data.
  12. Regular Model Auditing:Deep learning can aid in auditing models to identify potential privacy vulnerabilities or leaks, ensuring that models don't inadvertently expose sensitive information during inference.
These strategies highlight the potential synergy between deep learning and encryption/privacy techniques to create more secure and privacy-preserving cloud environments. However, it's essential to carefully design and validate these approaches to ensure their effectiveness and compliance with relevant privacy regulations.
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What is the blockchain architecture for IoT and can blockchain technology protect data confidentiality?
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Dear prof. Rk Naresh,
Info presented below may be helpful:
Blockchain architecture for IoT (Internet of Things) involves integrating blockchain technology into the IoT ecosystem to enhance security, data integrity, and trust among interconnected devices. IoT refers to the network of physical objects or "things" embedded with sensors, software, and other technologies that enable them to collect and exchange data over the internet. Blockchain, on the other hand, is a distributed and immutable ledger technology that can help address some of the security and privacy challenges in the IoT landscape.
The basic components of a blockchain architecture for IoT include:
  1. IoT Devices: These are the physical devices that gather and transmit data. They can range from sensors and actuators to wearable devices and industrial equipment.
  2. Data Collection and Transmission: IoT devices collect and transmit data over various communication protocols, such as Wi-Fi, Bluetooth, Zigbee, or cellular networks.
  3. Blockchain Network: A decentralized network of computers (nodes) that work together to maintain a shared ledger. Each node stores a copy of the blockchain, and consensus mechanisms are used to agree on the validity of transactions.
  4. Smart Contracts: Self-executing contracts with predefined rules and conditions. These contracts can automate processes based on data inputs and trigger actions when certain conditions are met.
  5. Consensus Mechanisms: Algorithms used to ensure that all nodes in the network agree on the state of the blockchain. Common mechanisms include Proof of Work (PoW), Proof of Stake (PoS), and others.
  6. Data Encryption: To ensure data privacy during transmission and storage, encryption techniques are used to protect data from unauthorized access.
  7. Private and Public Chains: Depending on the use case, blockchain networks can be public (open to anyone) or private (restricted to a specific group). Private chains might be preferred for certain IoT applications where data confidentiality is crucial.
  8. Data Integrity and Immutability: Once data is recorded on the blockchain, it becomes nearly impossible to alter or delete it, ensuring the integrity of the data.
Blockchain technology can indeed contribute to protecting data confidentiality in IoT by providing the following benefits:
  1. Decentralization: Traditional centralized systems are more susceptible to breaches as a single point of failure can compromise the entire system. Blockchain's decentralized nature reduces this risk.
  2. Data Encryption: Data can be encrypted before being stored on the blockchain, and only authorized parties with the decryption keys can access the data.
  3. Access Control: Blockchain-based systems can implement fine-grained access control mechanisms, ensuring that only authorized parties can access specific data.
  4. Immutable Audit Trail: Any data modification or access can be recorded on the blockchain, creating an audit trail that enhances accountability and reduces unauthorized tampering.
  5. User Consent Management: Blockchain can facilitate transparent and secure management of user consent for data sharing, enhancing user privacy.
While blockchain technology can enhance data confidentiality, it's important to note that it's not a one-size-fits-all solution. The architecture and implementation need to be tailored to the specific requirements and constraints of the IoT application. Additionally, blockchain technology also introduces challenges such as scalability, energy consumption (for PoW-based blockchains), and complexity in managing distributed networks. Therefore, a careful analysis of the trade-offs is necessary when considering blockchain for IoT applications.
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What are the potential cybersecurity vulnerabilities associated with interconnected robotic systems and automated processes? How can we safeguard against malicious attacks, data breaches, and unauthorized control that could compromise critical infrastructure or personal safety?
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Dear Abu Rayhan,
You may want to review the list presented below:
  1. Cybersecurity Vulnerabilities: Like any connected device, robots and automated systems can be vulnerable to cyberattacks. Hackers could potentially exploit security weaknesses to gain unauthorized access, take control of robots, steal sensitive data, or disrupt operations.
  2. Data Privacy Concerns: Robots and automation systems often collect and process large amounts of data. If not properly secured, this data could be at risk of unauthorized access, leading to privacy breaches and potential misuse of personal or sensitive information.
  3. Malware and Viruses: Malicious software or viruses could infect robotic systems, leading to operational disruptions, data loss, or even physical damage.
  4. Physical Security: Autonomous robots and automated systems in industrial settings could be manipulated or compromised to cause physical harm, damage equipment, or create safety hazards.
  5. Supply Chain Risks: Many robotics components and technologies are sourced globally. A compromised supply chain could introduce vulnerabilities or malicious code into the robotic systems.
  6. Human-Robot Interaction: As robots become more integrated into human environments, security risks related to interactions between humans and robots may arise. Malicious actors could manipulate robots to deceive, intimidate, or harm humans.
  7. Unintended Consequences: Automation systems may perform tasks autonomously based on algorithms and programming. If not properly designed and tested, these systems could make unintended or harmful decisions.
  8. Lack of Standardization: The lack of standardized security protocols and practices across different robotic platforms and manufacturers could lead to inconsistent security measures and potential vulnerabilities.
  9. Ransomware Attacks: Hackers could potentially lock or disable robotic systems and demand ransom for their release, disrupting critical operations and causing financial losses.
  10. Liability and Legal Issues: As robots become more autonomous, questions of liability arise in cases of accidents, malfunctions, or malicious actions carried out by robots.
  11. Ethical Concerns: Autonomous robots may be programmed to make decisions in certain situations. Ensuring that robots adhere to ethical guidelines and avoid harmful actions is a challenge that needs careful consideration.
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SMB's, Universities, hospitals, and non-profits struggle with CISA, HIPAA, and NIST compliance or best practices.
As cybersecurity professionals what are the main obstacles you face in assessment and getting stakeholders to upgrade as needed?
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Good compliances to best cybersecurity practices require sufficient empowerment of relevant professionals with managements' systematic attention, material and organizational support.
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Research Aim: Most data transmission is monitored by a firewall, which can also recognise and restrict undesirable traffic. The goal of this study is to highlight the potential features of a firewall while preventing cyber-attacks. The study aims to provide a detailed overview of the various categories of current and contemporary firewalls. The study uses a secondary research methodology to complete the research.
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Firewalls play a critical role in preventing cyber attacks and enhancing cybersecurity in network environments. Here are some key impacts of using a firewall:
Network Traffic Filtering: Firewalls act as a barrier between an organization's internal network and external networks, such as the internet. They analyze incoming and outgoing network traffic based on predefined security rules and policies. By filtering and inspecting network packets, firewalls can block unauthorized or malicious traffic while allowing legitimate communication. This helps to prevent unauthorized access and potential cyber attacks.
Access Control: Firewalls enable organizations to define access control policies for network resources. They can enforce rules that restrict or permit specific types of network connections based on criteria like IP addresses, ports, protocols, or application-level information. By controlling access to internal resources, firewalls help mitigate the risk of unauthorized access, data breaches, and other cyber threats.
Threat Detection and Prevention: Firewalls incorporate various security mechanisms to detect and prevent specific types of cyber threats. These mechanisms include:
Intrusion Detection System (IDS): Firewalls can integrate IDS functionality to identify and alert on suspicious or malicious network activities, such as port scanning, brute-force attacks, or known attack patterns.
Intrusion Prevention System (IPS): Advanced firewalls may include IPS capabilities that actively block or mitigate detected threats in real-time. IPS can automatically respond to suspicious activities, such as blocking malicious IP addresses or preventing specific network attacks.
Application Layer Filtering: Firewalls can inspect network traffic at the application layer to identify and block potentially malicious content or behavior. They can detect and prevent attacks like SQL injection, cross-site scripting (XSS), or malware downloads.
Network Segmentation: Firewalls facilitate network segmentation by dividing a network into multiple security zones. This allows organizations to isolate sensitive systems or data from less trusted areas. By enforcing strict communication rules between segments, firewalls limit the lateral movement of attackers in case of a breach, reducing the potential impact of an attack.
Logging and Auditing: Firewalls often provide logging and auditing capabilities, allowing organizations to monitor network traffic and security events. This information is valuable for incident response, forensic analysis, and compliance purposes. Firewall logs can help identify potential security incidents, track suspicious activities, and provide evidence in case of investigations.
It's important to note that firewalls are just one component of a comprehensive cybersecurity strategy. They should be complemented with other security measures such as regular patching, strong authentication mechanisms, employee awareness training, and proactive monitoring to ensure effective protection against cyber threats.
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Can Artificial Intelligence(AI) protect privacy? What will be the role of AI in cyber crime and future cyber security?
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Asiku,
I think, AI an excellent technique may be threat to data thieving in all-in-one system, automation, academic integrity, software, laboratory, writing, publishing, research, copy, cheating, plagiarizing, fishing, forensic, cyber data, developers, analyzers, audio, video, image processing, navel, aero, armed, hardware, business, financial organization. Biggest threat is that AI may accidently reveal sensitive data to all users and significant risk to privacy is likely to be remained controversial uses in the area of surveillance until multifaceted approach to data security is not taken.
Ashish
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Hi all - I am looking for an opportunity to review and Data Science and Cyber security paper. Any recommendation will help me.
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Hi,
Only Cyber Security paper/s could be review due to time constraints
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I am looking for an opportunity to review Cyber Security papers for any Journal, or conference. Any recommendation will help me.
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As far as I know they find you, if there is something of value in this regard in your CV. This is of course applicable to real scientific journals but not the predatory ones.
"Any recommendation will help me". You added. Yes there are many. One of my recommendations is to stay away from predatory journals and predatory conferences. Obviously you destroy your reputation as a scientist. I added this because you wrote "for any Journal, or conference."
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more important ....
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  1. Consensus Mechanisms: Implement robust consensus mechanisms, such as Proof of Work (PoW) or Proof of Stake (PoS), to prevent malicious actors from tampering with the data. These mechanisms ensure that any changes to the blockchain require the consensus of a majority of participants.
  2. Two-Factor Authentication (2FA): Implement 2FA mechanisms to ensure that only authorized participants can access and modify the blockchain. This adds an extra layer of security to protect against MITM attacks.
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Machine learning techniques can be applied to enhance anomaly detection in network traffic in a number of ways. Here are some of the most common methods:
  • Feature extraction: Machine learning algorithms can be used to extract features from network traffic data that can be used to identify anomalies. These features can include things like the source and destination IP addresses, the ports that are being used, the types of packets that are being sent, and the timing of the traffic.
  • Training: Once the features have been extracted, machine learning algorithms can be trained on a dataset of normal and anomalous network traffic. This training process allows the algorithms to learn the patterns that are associated with normal traffic and to identify anomalies that deviate from these patterns.
  • Detection: Once the machine learning algorithms have been trained, they can be used to detect anomalies in real-time network traffic. This can be done by comparing the features of the incoming traffic to the patterns that the algorithms have learned. If the incoming traffic deviates from these patterns, it is classified as an anomaly.
By using machine learning techniques, it is possible to enhance anomaly detection in network traffic and improve cybersecurity defenses. This is because machine learning algorithms can learn to identify patterns in network traffic that are indicative of malicious activity. This can help to identify and prevent cyberattacks before they cause damage.
Here are some of the benefits of using machine learning techniques for anomaly detection in network traffic:
  • Increased accuracy: Machine learning algorithms can be more accurate than traditional anomaly detection methods, such as rule-based systems. This is because machine learning algorithms can learn to identify patterns in network traffic that are indicative of malicious activity, even if these patterns are not explicitly defined.
  • Reduced false positives: Machine learning algorithms can also reduce the number of false positives that are generated by traditional anomaly detection methods. This is because machine learning algorithms are able to learn the patterns that are associated with normal traffic and to ignore traffic that deviates from these patterns but is not malicious.
  • Scalability: Machine learning algorithms can be scaled to handle large amounts of network traffic. This is important for organizations that have a large number of devices and users.
Overall, machine learning techniques can be a powerful tool for enhancing anomaly detection in network traffic and improving cybersecurity defenses.
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I am looking for research topic for my Phd which should be a combination of Cyber Security and ( Machine learning OR Deep Learning). Kindly assist with possible topic and objectives.
Thank you
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Dear Hamza Mouncif,
I propose the following topic for your thesis in the field of cyber security, machine learning, Deep Learning:
Improving cyber crime risk management systems and cyber security techniques and instruments for online and mobile banking systems through the application of selected Industry 4.0 technologies, i.e., among others, Blockchain, machine learning, deep learning, artificial intelligence, Big Data Analytics, multi-criteria simulation models, Internet of Things, etc. applied in a specific effective configuration. In the process of determining the effective configuration of applied selected technologies from the field of Industry 4.0, certain specialized applications running within Business Intelligence systems can help. The purpose of this type of research project is to use the available selected Industry 4.0 technologies to improve the cyber security of online and mobile banking systems, i.e. to increase the level of security for the development of such banking, online settlements and payments.
Best regards,
Dariusz Prokopowicz
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Please guide me on how can I find a topic for my thesis and where to start work.
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You have a general topic, now think about the skills you want to use. What skills and knowledge do you want to sharpen for your next job? What have you mastered? Do you like playing with data or writing and explaining models? Are your interests interdisciplinary?
At the end of your thesis, for one hot minute, you will be the world's expert in answering one question. What do you want that question to be?
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I am a student from Umar Musa Yaradua University Katsina
from Computer Science Department.
I need someone who is going to teach me cyber security.
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One great resource for learning about cybersecurity is the Cisco Networking Academy. They offer a free, self-paced course called "Introduction to Cybersecurity" that will teach you the basics of cybersecurity. You can find the course at this link: https://skillsforall.com/course/introduction-to-cybersecurity
Once you've completed the introductory course, you can continue your education with Cisco's other cybersecurity courses. They offer a variety of courses at different levels, so you can find one that's right for your skill level.
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How can artificial intelligence help improve cyber security systems and techniques in online and mobile banking?
In recent years, technologies that play an important role in the field of Industry 4.0, technologies that determine the current fourth technological revolution, including machine learning technologies, deep learning and artificial intelligence are finding their way into improving cyber security systems. Financial institutions, including commercial banks, are implementing a certain generation of artificial intelligence technologies into specific areas of banking in order to increase process efficiency, economic efficiency, etc. Artificial intelligence is already being used by banks to improve remote communication processes with customers, in call centre departments as part of the creation of automated remote communication systems and digitised adviser avatars, in the process of managing credit risk and other risk categories, and also in the area of cyber-security.Therefore, opportunities are emerging for the application of artificial intelligence technologies in improving cyber-security techniques and systems in various areas of cyber-security in online and mobile banking, including issues such as:
1. improving cyber-security techniques to protect the information systems of financial institutions, including banks, from external cyber-attacks via email and ransomware viruses.
2. improving cybercrime risk management systems for integrated internal information systems connected to the Internet.
3. Improving cyber security techniques implemented through the implementation of new Industry 4.0 technologies, including artificial intelligence, machine learning, Blockchain, multi-criteria simulation models, etc., and advanced analytical techniques such as Big Data Analytics, Business Intelligence.
4. improving the anti-spam systems used to protect email inboxes in order to increase the filtering out of emails where cybercriminals use phishing techniques and/or send malware and ransomware viruses in the background of the email.
5. Improving cyber-security techniques for online and mobile banking systems with a particular focus on mobile banking implemented on the bank's customer side using smartphones equipped with specific operating systems and web applications.
6. Analysis of the level of effectiveness and cyber security for biometric techniques for encoding access to information systems as an important factor in cyber security.
7. perfecting cyber-security techniques in integrated, internal, intranet information systems operating in financial institutions, including commercial banks characterised by a complex organisational structure and companies forming interconnected business entities.
In view of the above, financial institutions, including commercial banks, are implementing a certain generation of artificial intelligence technologies in various areas of banking, including in the improvement of cyber-security techniques and systems.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
How can artificial intelligence help improve cyber-security systems and techniques in online and mobile banking?
What do you think about this topic?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Best regards,
Dariusz Prokopowicz
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Artificial intelligence (AI) has the potential to significantly improve cybersecurity systems and techniques. Here are some of the ways that AI can be used to improve cyber security:
  • Automated threat detection: AI can be used to automate the process of threat detection, freeing up human security analysts to focus on more critical tasks. AI-powered threat detection systems can analyze vast amounts of data in real-time, identifying potential threats that would be difficult for humans to detect.
  • Malware detection: AI can be used to detect malware, even new and unknown malware. AI-powered malware detection systems can learn to recognize patterns in malware code, even if the malware has been designed to evade traditional detection methods.
  • Intrusion detection: AI can be used to detect intrusions into computer systems. AI-powered intrusion detection systems can learn to recognize patterns of behavior that are indicative of an intrusion, even if the intrusion is being carried out by a human or a bot.
  • Network security: AI can be used to improve network security. AI-powered network security systems can learn to identify and block malicious traffic, even if the traffic is disguised to look like legitimate traffic.
  • User behavior analysis: AI can be used to analyze user behavior to identify potential threats. AI-powered user behavior analysis systems can learn to recognize patterns of behavior that are indicative of a malicious attack, even if the attack is being carried out by a human or a bot.
Overall, AI has the potential to significantly improve cybersecurity systems and techniques. By automating tasks, detecting threats, and analyzing behavior, AI can help to make computer systems and networks more secure.
Here are some additional benefits of using AI in cybersecurity:
  • Reduces human error: AI can help to reduce human error in cybersecurity by automating tasks and providing insights that would otherwise be missed by human analysts.
  • Improves efficiency: AI can help to improve the efficiency of cybersecurity operations by automating tasks and providing insights that can be used to prioritize threats.
  • Increases visibility: AI can help to increase visibility into cybersecurity threats by providing insights into patterns of behavior and traffic that would otherwise be difficult to detect.
  • Provides new capabilities: AI can provide new capabilities for cybersecurity that were not previously possible, such as the ability to detect and respond to zero-day threats.
Of course, there are also some challenges associated with using AI in cybersecurity. These challenges include:
  • Data requirements: AI-powered cybersecurity systems require large amounts of data to train and operate. This data can be difficult and expensive to collect, and it can be difficult to ensure that the data is accurate and representative of the threats that an organization faces.
  • Bias: AI systems can be biased, which can lead to false positives or false negatives. This bias can be introduced into the system through the data that is used to train the system, or through the way that the system is designed.
  • Interpretability: AI systems can be difficult to interpret, which can make it difficult to understand why the system made a particular decision. This can make it difficult to trust the system and to use it to make critical decisions.
Despite these challenges, the potential benefits of using AI in cybersecurity are significant. As AI technology continues to develop, it is likely that AI will play an increasingly important role in cybersecurity.
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Abstract MANET is a wireless network that lacks infrastructural support. Most operations they perform are coordinated between nodes because of their limited transmission range. Assess the secure MANET routing protocol in a malicious environment. However, in the MANET environment security is an important constraint that affects the data transmission rate and cause data loss. This paper developed an Intrusion Detection System Conditional RandomField (IDS – CRF) model for security. The developed model uses the hashing function integrated with the Conditional Random Field model. The IDS – CRF model uses the label sequences for data security in the MANET environment. The model uses the DSR protocol with the discriminative field to process the secure data in the MANET. The proposed IDS-CRF model uses the hashing function for improved data transmission security in the MANET environment. The performance of the proposed IDS – CRF model is comparatively examined with the conventional DSR protocol. The comparative analysis expressed that the proposed IDS – CRF model achieves the ~4% reduction in the delay and ~2% reduction in the routing overhead. Similarly, the proposed IDS – CRF model achieves the ~4% reduced energy consumption. In terms of the Packet delivery ratio and throughput the proposed IDS – CRF model achieves the ~4% - ~6% improved performance than DSR model.
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Technology and the economy are deeply interconnected, and advancements in technology have had a significant impact on the economic sector, and vice versa. The relationship between technology and the economy can be viewed from several perspectives:
Innovation and Technology Adoption: Technological advancements have enabled the creation of new products and services, and businesses that innovate and adopt new technologies can gain a competitive advantage over their peers. The adoption of new technology can also lead to increased productivity, cost savings, and improved customer experiences, all of which can positively impact the economic sector.
Automation and Job Disruption: The adoption of new technologies can also lead to job displacement, particularly in industries that rely heavily on manual labor. However, the automation of certain tasks can also lead to the creation of new jobs in the technology sector. Additionally, the increased efficiency and productivity resulting from automation can lead to economic growth and increased job opportunities in other sectors.
Data Analytics and Decision Making: The use of data analytics and artificial intelligence in business decision making has become increasingly prevalent, as companies seek to gain insights into consumer behavior and improve operational efficiency. These technologies can provide businesses with a competitive advantage by enabling them to make data-driven decisions and improve their bottom line.
E-commerce and Globalization: The growth of e-commerce has enabled businesses to expand their customer base globally, leading to increased trade and economic growth. Additionally, the rise of digital platforms has enabled small businesses to compete with larger firms and enter new markets.
Overall, technology and the economy are closely intertwined, and advancements in technology have the potential to shape the future of the economic sector in significant ways. However, the impact of technology on the economic sector is not always positive, and policymakers must consider the potential impact of new technologies on the workforce and society as a whole.
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Challenges confronting CISOs are evolving beyond technology, cybersecurity, and controls, as Gartner predictions for 2023 reveal. “By 2025, lack of talent or human failure will be responsible for over half of significant cyber incidents."
How can modern cybersecurity leaders strengthen their programs in the face of these challenges?
“By 2025, the consumerization of AI-enabled fraud will fundamentally change enterprise attack surface driving more outsourcing of enterprise trust and focus on security education and awareness.”
We believe, in this report you will learn key findings, market implications, and recommendations on:
  • Designing a cybersecurity program that leverages data to improve decision making
  • Demonstrating how a strong security performance is relevant to winning business
  • Integrating cybersecurity into the overall business with shareable results
  • Effectively allocating budget to both technology and education to establish program consistency
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Since it's assumed that P != NP, cyberspace is safe according to SSL protocol.
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Blockchain enables secure and transparent data sharing, encrypted with cryptographic techniques to maintain privacy. It offers solutions for identity management, smart contracts, and auditing, ensuring secure transactions and facilitating compliance. Potential applications of blockchain in cybersecurity include secure transactions and payments, supply chain security, decentralized identity management, IoT security, cyber threat intelligence sharing, and secure cloud storage. As blockchain technology continues to evolve, it is expected to have an increasingly significant role in enhancing security and trustworthiness across industries.
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This research question focuses on the specific domain of cybersecurity, namely the Internet of Things (IoT). IoT devices are becoming increasingly prevalent in various sectors, including healthcare, transportation, and smart homes, but they also pose unique security risks. Investigating the emerging threats and challenges associated with securing IoT devices and networks can provide valuable insights into the vulnerabilities and potential countermeasures needed to protect these interconnected systems. This research inquiry can contribute to the development of robust security frameworks, protocols, and strategies for IoT deployments, ultimately enhancing the overall cybersecurity landscape.
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The Internet of Things (IoT) is a rapidly growing network of physical devices that are connected to the internet. These devices collect and exchange data, which can be used to improve efficiency, safety, and convenience. However, the IoT also introduces new security challenges. Some of the emerging threats and challenges in securing IoT devices and networks include:
  • The increasing number of IoT devices: The number of IoT devices is growing rapidly, and this makes it difficult to keep track of all of them. This can make it easier for attackers to target IoT devices, as they may not be as well-protected as traditional IT systems.
  • The limited security capabilities of IoT devices: Many IoT devices have limited security capabilities, as they are designed to be low-cost and easy to use. This makes them more vulnerable to attack, as they may not be able to withstand sophisticated attacks.
  • The lack of visibility into IoT networks: It can be difficult to get visibility into IoT networks, as they may be spread across multiple locations and organizations. This makes it difficult to identify and respond to security threats.
  • The use of open-source software in IoT devices: Many IoT devices use open-source software, which can be a security risk. This is because open-source software is often more complex and difficult to secure than proprietary software.
  • The lack of security standards for IoT devices: There are no universally accepted security standards for IoT devices. This makes it difficult to ensure that IoT devices are secure, as there is no common baseline for security.
These are just some of the emerging threats and challenges in securing IoT devices and networks. As the IoT continues to grow, it is important to be aware of these challenges and to take steps to mitigate them. Here are some of the things that can be done to secure IoT devices and networks:
  • Use strong passwords and authentication methods: IoT devices should use strong passwords and authentication methods, such as multi-factor authentication.
  • Keep IoT devices up to date with the latest security patches: IoT devices should be kept up to date with the latest security patches, as these patches often address security vulnerabilities.
  • Use a firewall to protect IoT devices: A firewall can be used to protect IoT devices from unauthorized access.
  • Use encryption to protect data: Data that is transmitted between IoT devices and networks should be encrypted to protect it from unauthorized access.
  • Monitor IoT devices for suspicious activity: IoT devices should be monitored for suspicious activity, such as unauthorized access or data exfiltration.
By taking these steps, organizations can help to secure their IoT devices and networks and protect themselves from emerging threats.
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This question looks at practical examples of how blockchain technology has been implemented to enhance cybersecurity in various industries, such as supply chain management, healthcare data sharing, and identity verification.
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Blockchain technology has been used to improve cybersecurity in a number of real-world use cases, including:
  • Supply chain management: Blockchain can be used to track the movement of goods and materials through a supply chain, making it more difficult for counterfeit or tampered products to enter the supply chain.
  • Identity management: Blockchain can be used to create secure and tamper-proof digital identities, which can help to prevent identity theft and fraud.
  • Data security: Blockchain can be used to store sensitive data in a secure and tamper-proof manner. This can be useful for storing financial data, medical records, or other sensitive information.
  • Smart contracts: Blockchain-based smart contracts can be used to automate transactions and agreements, reducing the risk of human error and fraud.
  • Cybersecurity research: Blockchain technology can be used to store and share cybersecurity research data in a secure and tamper-proof manner. This can help to accelerate the pace of cybersecurity research and development.
These are just a few examples of how blockchain technology is being used to improve cybersecurity. As the technology continues to mature, we can expect to see even more innovative use cases emerge.
Here are some additional details about each of these use cases:
  • Supply chain management: Blockchain can be used to track the movement of goods and materials through a supply chain by creating a shared ledger that records each transaction. This ledger is distributed across all participants in the supply chain, making it difficult for anyone to tamper with the data. This can help to prevent counterfeit or tampered products from entering the supply chain, as well as help to identify and address any problems that may occur.
  • Identity management: Blockchain can be used to create secure and tamper-proof digital identities by using cryptographic techniques to store identity information. This information can be used to verify a user's identity when they log in to a website or application, or to make payments online. This can help to prevent identity theft and fraud.
  • Data security: Blockchain can be used to store sensitive data in a secure and tamper-proof manner by using cryptographic techniques to encrypt the data. This data can then be stored on the blockchain, which is a distributed ledger that is maintained by a network of computers. This makes it very difficult for anyone to access the data without the correct permissions.
  • Smart contracts: Blockchain-based smart contracts can be used to automate transactions and agreements by using self-executing code. This code is stored on the blockchain and is executed when certain conditions are met. This can help to reduce the risk of human error and fraud, as well as speed up the process of executing transactions.
  • Cybersecurity research: Blockchain technology can be used to store and share cybersecurity research data in a secure and tamper-proof manner. This can help to accelerate the pace of cybersecurity research and development by making it easier for researchers to share data and collaborate on projects.
These are just a few examples of how blockchain technology is being used to improve cybersecurity. As the technology continues to mature, we can expect to see even more innovative use cases emerge.
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This question aims to highlight the fundamental distinctions between traditional cybersecurity approaches, which often rely on centralized control and trusted third
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Traditional cybersecurity approaches and blockchain-based security differ in several key aspects. Here are the main differences between the two:
1. Centralized vs. Decentralized: Traditional cybersecurity relies on centralized systems, where security measures are implemented and managed by a central authority or organization. In contrast, blockchain-based security operates on a decentralized network of computers (nodes) that collectively validate and secure transactions and data.
2. Trust Model: Traditional cybersecurity approaches typically require trust in centralized authorities, such as governments or institutions, to maintain security. Blockchain-based security, on the other hand, employs a trustless model, where trust is distributed across the network through consensus algorithms. This eliminates the need for a central authority and allows participants to interact directly with each other.
3. Data Integrity: Traditional cybersecurity primarily focuses on securing data through encryption, access controls, firewalls, and other conventional measures. Blockchain-based security utilizes cryptographic techniques to ensure data integrity. Data stored on a blockchain is tamper-evident, as each block contains a cryptographic hash of the previous block, making it extremely difficult to modify historical data without detection.
4. Immutable Records: Blockchain technology provides an immutable and transparent ledger that records all transactions and data modifications. Once a transaction or data entry is recorded on the blockchain, it cannot be altered or deleted, creating a reliable and auditable trail of activities. This feature enhances transparency and reduces the risk of data manipulation.
5. Smart Contracts: One of the unique aspects of blockchain-based security is the use of smart contracts. Smart contracts are self-executing contracts with predefined rules encoded on the blockchain. They automatically execute transactions and enforce agreements when specific conditions are met. Smart contracts enhance security by reducing the need for intermediaries, minimizing human error, and ensuring that transactions occur as intended.
6. Resilience to Attacks: Traditional cybersecurity measures are susceptible to various types of attacks, such as DDoS attacks, data breaches, and ransomware. Blockchain-based security, due to its decentralized nature and consensus mechanisms, offers enhanced resilience against these attacks. An attacker would need to compromise a majority of the network's computing power to alter the blockchain's data, making it highly challenging and computationally expensive.
7. Privacy and Anonymity: Traditional cybersecurity often involves the collection and storage of personal data, which can raise privacy concerns. Blockchain-based security offers the potential for improved privacy and anonymity through techniques such as zero-knowledge proofs and private or permissioned blockchains. These techniques allow users to prove the validity of information without revealing the underlying data, enhancing privacy while still maintaining security.
It's worth noting that while blockchain-based security has unique advantages, it is not a one-size-fits-all solution. The technology has its own limitations and may not be suitable for all cybersecurity use cases. Evaluating the specific requirements and context is crucial when considering the adoption of blockchain-based security.
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justifiy your answer please........
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One of the key challenges in ensuring cybersecurity in blockchain implementations is the vulnerability of smart contracts to security breaches. Smart contracts are self-executing code stored on the blockchain, and if they contain bugs or are not properly audited, they can be exploited by attackers to gain unauthorized access or manipulate the system. To address this challenge, thorough code reviews, rigorous testing, and formal verification techniques can be employed to identify and fix vulnerabilities before deployment. Additionally, ongoing monitoring and regular updates to address emerging threats are essential. Another challenge is the potential for 51% attacks, where a single entity or a colluding group controls the majority of the blockchain's computational power, allowing them to manipulate transactions or block confirmations. To mitigate this risk, blockchain networks can adopt consensus mechanisms that are resistant to such attacks, such as proof-of-stake (PoS) or practical Byzantine fault tolerance (PBFT), and ensure a diverse and decentralized distribution of computational power among network participants. Furthermore, strong encryption and authentication mechanisms should be implemented to protect data in transit and at rest, and comprehensive access control policies should be enforced to limit unauthorized access to sensitive information.
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Despite making significant investments in security technologies, organizations continue to struggle with security breaches: Their adversaries are quick to evolve tactics and stay ahead of the technology curve. Humans may soon be overwhelmed by the sheer volume, sophistication, and difficulty of detecting cyberattacks. People are already challenged to efficiently analyze the data flowing into the security operations center (SOC) from across the security tech stack. This doesn’t include the information feeds from network devices, application data, and other inputs across the broader technology stack that are often targets of advanced attackers looking for new vectors or using new malware. And as the enterprise increasingly expands beyond its firewalls, security analysts are charged with protecting a constantly growing attack surface.
source: The future of cybersecurity and AI | Deloitte Insights
Applied Sciences | Special Issue: Smart Cyberspace and IoT Systems: Challenges and Future Trends (mdpi.com)
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Cyber Security Challenges in the IoT Cyber Critical Infrastructures
The Internet of Things (IoT) is a rapidly growing network of physical devices that are connected to the Internet. These devices collect and exchange data, which can be used to improve efficiency, productivity, and safety. However, the IoT also introduces new cybersecurity challenges. Some of the most common cyber security challenges in the IoT include:
  • Device vulnerabilities: IoT devices are often designed with security in mind. However, they are often not as secure as traditional IT devices. This is because IoT devices are often low-cost and have limited resources.
  • Data security: IoT devices collect and exchange large amounts of data. This data can be sensitive, such as personal information or financial data. If this data is compromised, it can lead to identity theft, fraud, and other financial losses.
  • Network security: IoT devices are often connected to the internet. This means that they are vulnerable to attacks from hackers. Hackers can use these devices to gain access to corporate networks and steal data.
  • Physical security: IoT devices are often located in public places. This makes them vulnerable to physical attacks, such as theft or vandalism.
Recent AI driven cybersecurity implementations
There are a number of AI-driven cybersecurity implementations that are being used to address the cyber security challenges in the IoT. Some of the most common AI-driven cybersecurity implementations include:
  • Machine learning: Machine learning is being used to develop algorithms that can identify and respond to cyber threats. Machine learning algorithms can be trained on large datasets of known cyber threats. This allows the algorithms to learn to identify new threats that are similar to known threats.
  • Artificial intelligence: Artificial intelligence is being used to develop systems that can automatically respond to cyber threats. Artificial intelligence systems can be used to automatically block malicious traffic, quarantine infected devices, and restore systems that have been compromised.
  • Blockchain: Blockchain is a distributed ledger technology that is being used to create secure and tamper-proof records of transactions. Blockchain can be used to store data from IoT devices in a secure and tamper-proof manner. This can help to protect data from hackers.
These are just a few of the cybersecurity challenges in the IoT and some of the recent AI-driven cybersecurity implementations that are being used to address these challenges. As the IoT continues to grow, it is important to develop new and innovative cybersecurity solutions to protect IoT devices and the data that they collect.
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Right now, chatgpt is known by everyone. What we think about chatgpt + cyber security?
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In the offensive world, i.e. pentesters, red teamers it is pretty usefull. The keyword here would be 'offensive chatgpt'
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hello,
I'm a presently running my master degree programme in cybersecurity and i am looking at Digital Forensics and Information security management. I really need good topics and materials on it. Can anyone help please with topics along that path and materials also?
Thanks
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You might find the article below relevant, and it has some interesting references you might want to contact.
This is a very hot topic right now.
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I need to understand what & how to choose sampling techniques I should pick to explore cryptography issues in cyber security domain.
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To start on research design and sampling techniques in cybersecurity and cryptography, the following steps may be helpful:
1. Define research problem: Identify a topic that is worth studying. It could be a security vulnerability that needs to be addressed or a cryptographic algorithm that needs to be studied.
2. Literature Review: Review existing studies and literature related to the topic. It will help understand the research gaps and research questions.
3. Research questions: Develop research questions that will guide the research study. For example, "What are the common cyber-attacks on financial institutions, and what are the effective cryptography techniques to defend against them?"
4. Research design: Select a research design that allows answering the research questions effectively. It could be experimental, survey-based, observational, etc.
5. Sampling Techniques: Determine the appropriate sampling technique that will help in selecting the participants from the population. In cybersecurity and cryptography, it is essential to choose participants who have knowledge and experience in the field.
6. Data Collection: Collect data that aligns with the research design and research question. For instance, if the research design is a case study, collect data through interviews, observation, and documentation.
7. Data Analysis: Analyze the collected data to answer the research questions. It can be done by applying qualitative or quantitative analysis methods.
8. Draw Conclusions: Based on the analysis, draw conclusions from the research study, and provide recommendations.
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Currently l am thinking of perusing research computing, ideal area cybersecurity problems and IoT, so far proposed research title "security risk assessment in IoT systems: Data privacy and security" any suggestions please experts
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There are many current problems in cybersecurity and IoT, some of which include:
1. Device vulnerabilities: IoT devices are often designed with weak security, making them an easy target for hackers.
2. Distributed Denial-of-service (DDoS) attacks: These attacks are increasing in frequency and are becoming more sophisticated, making them difficult to prevent.
3. Lack of standards: There is currently a lack of industry-wide standards for IoT security, making it difficult for manufacturers to ensure that their devices are secure.
4. Data privacy: IoT devices often collect and store large amounts of data, putting personal and sensitive information at risk.
5. Insider threats: Employees, contractors, and other insiders can pose significant security risks to IoT systems, as they can intentionally or inadvertently cause data breaches.
6. Cloud security: Many IoT devices rely on cloud services to operate, making them vulnerable to cloud-based attacks.
To address these problems, it is important for businesses and individuals to implement strong cybersecurity measures, such as strong passwords, encryption, and regular updates. Additionally, industry-wide standards and regulations can help ensure that IoT devices are designed and manufactured with security in mind.@
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Dear ResearchGate members,
My name is Txema Romero. I live and work in Barcelona (Europe).
I am interested in implementing cybersecurity measures in the healthcare sector.
I would like to write a technical report about how to apply cybersecurity to the healthcare sector. It would be a review of the different tools and how to apply them (e.g, for ethical hacking, risk analysis, risk mitigation action plans, cybersecurity design directives, etc.).
Could you please share with me any information related to this topic?
Thank you. I look forward to hearing from you.
Regards,
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This is the topic of the Horizon Europe project NEMECYS (soon available at https://www.nemecys.eu). The first public deliverables will be submitted next month, so watch this space.
In the meantime, here are some previous papers on the topic:
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Dear Professor
I hope this message finds you well. I am writing to ask for your support in voting for my research paper for the Cybersecurity Award in [Track A--- Best Theoretical Research Paper]. My paper is titled [Securityand Privacy in Internet of Things] and it presents innovative findings in Cybersecurity.
As you may know, this award recognizes outstanding contributions to the field and winning it would be a great honor for me. I believe my paper deserves to win because it makes a significant contribution to the field and has the potential to impact future research in the area.
I would be grateful if you could take a few seconds to vote for my paper. You can find more information about the award and the voting process on the [Cybersecurity Award 2023] website [https://cybersecurity.springeropen.com/]. The deadline for voting is [26 May 2023].
Thank you for your time and support. Please let me know if you have any questions or need more information about my research paper.
Best regards,
[Md AlimulHaque]
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Yes
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Vote for my research paper 2023
Dear Professor
I hope this message finds you well. I am writing to ask for your support in voting for my research paper for the Cybersecurity Award in [Track A--- Best Theoretical Research Paper]. My paper is titled [Securityand Privacy in Internet of Things] and it presents innovative findings in Cybersecurity.
As you may know, this award recognizes outstanding contributions to the field and winning it would be a great honor for me. I believe my paper deserves to win because it makes a significant contribution to the field and has the potential to impact future research in the area.
I would be grateful if you could take a few seconds to vote for my paper. You can find more information about the award and the voting process on the [Cybersecurity Award 2023] website [https://cybersecurity.springeropen.com/]. The deadline for voting is [26 May 2023].
Thank you for your time and support. Please let me know if you have any questions or need more information about my research paper.
Best regards,
[Md AlimulHaque]
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Done
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More of Ehical hacking.
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Becoming a good cybersecurity professional requires a combination of education, training, and experience. for examble
1. Get an education: Pursue a degree or diploma in cybersecurity or a related field, such as computer science, information technology, or engineering. This will provide you with foundational knowledge of computer systems, networks, and security concepts.
2. Develop technical skills: Familiarize yourself with the different tools and software used in cybersecurity, including vulnerability scanners, antivirus software, and intrusion detection and prevention systems. Some of the technical skills that are important in cybersecurity include programming, data analysis, and system administration.
3. Gain practical experience: Consider internships or entry-level jobs in cybersecurity to get real-world experience. This will help you apply the theoretical knowledge you have gained in your education to real-world situations.
4. Stay up-to-date: Stay current with the latest cybersecurity trends and news by reading industry blogs and news sites, attending conferences and workshops, and becoming familiar with the cybersecurity community.
5. Get certified: Certification can validate your skills and knowledge in specific areas of cybersecurity. Some popular certifications include Certified Information Systems Security Professional (CISSP), Certified Ethical Hacker (CEH), and Certified Information Security Manager (CISM).
6. Develop soft skills: Effective communication, collaboration, and problem-solving skills are critical in cybersecurity, as it's a team-based approach.
Keep in mind that cybersecurity is a constantly evolving field, and you will need to continually learn and update your skills to stay relevant.
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Please suggest me Ph.D. topic on cybersecurity particularly considering either app security, context-aware systems, and small devices including but not limited to embedded control systems, IoT applications, and sensor networks. Or a topic relates to AI & Machine Learning.
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Here are a few potential PhD topics in cyber-security or machine learning:
  1. Adversarial Machine Learning: Develop new machine learning models and algorithms that are robust against adversarial attacks, which are malicious attempts to manipulate or deceive machine learning systems.
  2. Explainable AI for Cyber-security: Investigate methods for making machine learning models more transparent and interpretable, so that cyber-security experts can better understand how these models make decisions and detect potential vulnerabilities.
  3. Cyber Threat Intelligence: Develop new methods for collecting, analyzing, and sharing information about cyber threats, with the goal of improving early detection and response to cyber attacks.
  4. Privacy-Preserving Machine Learning: Investigate methods for training machine learning models on sensitive data without compromising individual privacy, using techniques such as differential privacy and federated learning.
  5. Malware Detection and Analysis: Develop new techniques for detecting and analyzing malware, using machine learning algorithms to identify patterns and behaviors that are indicative of malicious activity.
  6. Cyber-security for the Internet of Things (IoT): Investigate new methods for securing IoT devices and networks against cyber attacks, using machine learning algorithms to detect and respond to potential threats.
These are just a few examples of potential PhD topics in cyber-security or machine learning. It's important to choose a topic that aligns with your interests and expertise, and that has the potential to make a significant contribution to the field.
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IIoT 4.0 and 5.0 security discussion.
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Users can not be secure from creators like living beings what ever try but die after some time which is hopefully decided by power governing life. Blind being expert to identity sounds feels proud to walk on Highways, crashes by in EV in cluster of heavy vehicles.
Cyber security layer whatever be, in security layer will be +1 or more. Torget of Cyber security level in infinity ( starting from rate of sampling of analogue).
It is view of development of cybersecurity.
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"Which topics do you recommend for computer engineering with a focus on cyber security and deep learning, or any other hot topics suitable for a PhD degree in computer and communication engineering? I am in the early stages of my research and would appreciate any suggestions, including relevant papers. Additionally, I am seeking a co-supervisor to guide me throughout my research."
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Computer and information security i.e data security
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Currently the cybersecurity is reactive. It reacts to the presence of certain signatures in the operating system, applications, middleware, protocols, frameworks and networks. When these signatures are detected, the appropriate patches are applied to plug the vulnerabilities represented by the signatures.
In this reactive approach, the initiative remains with the attacker. The defense is reactive to what the attacker does. And there is always a time lag between the initiative by the attacker and the construction of the corresponding signatures.
This is not a winning strategy.
Cybersecurity has taken many directions, trends, and new ideas. But this scenario has not changed, the strategy remain a strategy that is not a winning strategy.
In human based decision making, the law enforcement preempts the offender, and arrests the offender before the offence has taken place.
In cybersecurity, we need such a strategy. This would preempt the attacker. It would stop the attack before it begins.
Choudhary Associates in Bowie, MD, have a product that can accomplish this. That constitutes a winning strategy. The details and operations are proprietary. They cannot be disclosed. But the product can be demonstrated in operational scenarios.
Who do you think is a potential customer? How do you think the product can be marketed (currently there is no sales and marketing budget).
Would you be willing to invest in further development, marketing and sales? Who would be interested? Who could be a financier?
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Cybersecurity based on signatures and patches is not a winning strategy
Cybersecurity based on signatures and patches is not a winning strategy. Signature-based detection only works when the threat actor is using known malware, which they often are not. In addition, patches are not always effective against zero-day threats. A better strategy is to use a layered approach to security, which includes monitoring and controlling network traffic, using multiple layers of defense such as firewalls, antivirus software, and intrusion detection systems, and educating users on safe computing practices
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Good day All, I just registered for a Post Grad Diploma in ICT. What are the latest topics that I can tackle for my research. I am interested in Cybersecurity but I just don't know where to begin choosing a topic.
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Application if Blockchain to security.
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"Which topics do you recommend for computer engineering with a focus on cyber security and deep learning, or any other hot topics suitable for a PhD degree in computer and communication engineering?
I am in the early stages of my research and would appreciate any suggestions, including relevant papers. Additionally, I am seeking a co-supervisor to guide me throughout my research."
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Dear Omar,
First, good luck with your scientific career.
I suggest for you the following topic: SON (Self-organizing Networks) based on Reinforcement Learning (RL) or Deep Learning to meet 6G requirements.
If you are interested to work on this topic, I can support you via remote co-supervising. I suggest also discussing the topic with your supervisor.
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What mechanisms are adequate to effectively protect nations' critical infrastructures. I believe that it is necessary to develop new cybersecurity frameworks that take risk analysis as a solid basis for protecting them.
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The NIST Cyber Security Framework is an excellent approach to understanding defense from an attacker's perspective. It is as light weight as one wants it to be, demonstrated by its global adoption. In terms of risk management frameworks, the most important element is to conduct an analysis of the field to have information that first describes and next can be used to reduce the risk level.
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Non-repudiation is a legal term defined as protection against an individual falsely denying having performed a particular action. It provides the capability to determine whether a given individual took a specific action, such as created information, approved information or sent or received a message.
Social media platforms such as WhatsApp have a function which allows one to send a message to an individual or a group and DELETE the message from all the recipients leaving them with no evidence of what has been sent. This allows the sender to deny having sent such a message. Read-once messages which get destroyed upon being read can also be a breach of non-repudiation on Social Media platforms.
What is your view of these functions on social media platforms? Legally are they acceptable?
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I don't use whatsapp and I don't care to use it.
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This question is related to cyber security as we are receiving logs and flows as many times we are not able to capture usernames in many events.
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While capturing usernames in logs and flows can be important for cybersecurity, there are challenges to doing so, and it is important to have logging best practices in place to ensure that relevant information is captured and stored securely.
Generally, capturing usernames (and other sensitive information) can be a security risk if it is not done properly. If usernames are captured and stored insecurely, they can be accessed by unauthorized individuals and used for malicious purposes such as identity theft or account takeover.
To prevent this, websites and applications typically for example use secure methods for capturing and storing usernames, such as encryption and hashing. These methods can make it difficult or impossible for unauthorized individuals to access the usernames even if they are able to gain access to the website or application's database.
In the context of cybersecurity, it is important to capture as much information as possible in logs and flows to help identify and respond to security incidents.
The reason why usernames may not always be captured in logs or flows is that some systems or applications may not log this information, or the logs may not be configured to capture it. Additionally, some attackers may try to obfuscate their usernames or use tactics such as password spraying, which makes it more difficult to identify a specific user account.
Otherwise, depending on the context and purpose of the "flows" you are referring to, it may not be necessary or appropriate to capture usernames. For example, if you are designing a user flow for a public-facing website or application, you may want to allow users to browse and use certain features without requiring them to create an account or provide personal information.
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Is important cybersecurity experts know about research methodologies?
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No its not necessary. Concept and problem finders should adhere to the situational analysis. that's why they should be more knowledgeable in the core area. He should be techno-savvy and understand the basic and upcoming concept of cyber-security. Research instruments can be developed.
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NIST's new cybersecurity profile is designed to help mitigate risks to systems that use positioning, navigation and timing (PNT) data, including systems that underpin modern finance, transportation, energy and other critical infrastructure. While its scope does not include ground- or space-based PNT source signal generators and providers (such as satellites), the profile still covers a wide swath of technologies.
Source:
Safeguarding Critical Infrastructure: NIST Releases Draft Cybersecurity Guidance, Develops GPS-Free Backup for Timing Systems | NIST
Assessing Cyber Threats To Canadian Infrastructure - Canada.ca
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Firewalls
Antivirus Software.
PKI Services.
Managed Detection and Response Service (MDR)
Penetration Testing.
Staff Training.
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The SCPS Lab (https://www.scpslab.org/) is hiring for two Ph.D. positions in the following areas:
  • Federated Defense Against Adversarial Attacks in IIoT.
  • Threat and Anomaly Detection for Cloud Security.
The required skills for potential graduate students include:
  • Strong background in cyber security.
  • Strong background in machine learning and data analytic techniques.
  • Background in detection and estimation theory.
  • Strong oral and written communication skills.
To apply, please contact Dr. Hadis Karimipour (hadis.karimipour@ucalgary.ca) with your most recent C.V. and a list of two references.
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Great to know
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Hi everyone
I need new ideas for a Ph.D. Degree in Cybersecurity.
Thank you in advance
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I have a research on cyber security. Where i worked with patterns of cyber attacks actually and one of the hypothesis of the research is: Cyber-attack gives the same pattern in all the different, different IP addresses. You can check the research from my profile to get some big ideas for you.
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Humans represent a mystery to be deciphered by security/cybersecurity experts because their behaviors, attitudes, beliefs, rituals and decisions (the general characteristics that define a culture) constitute a little-understood universe for executives and their heads of security. Frequently cited in various international research projects and reports is the fact that people are the weakest links in the security chain. Time and again, it is determined that, despite all the technical efforts and security procedures, people are highly likely to expose organizations to vulnerabilities.
source:
The Human Factor in Information Security (isaca.org)
“Chain” is defined here as the sequence of connected links that enables a system to function. Its strength is defined in terms of the connection that is least strong.
Dreyer, P.; T. Jones; K. Klima; J. Oberholtzer; A. Strong; J. Welburn; Z. Winkelman; “Estimating the Global Cost of Cyber Risk: Methodology and Examples,” Rand Corporation, 2018, https://www.rand.org/pubs/research_reports/RR2299.html
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In today's globalized and digitally connected world, cyber security has become an essential aspect of our lives. With the increasing reliance on technology, the potential for cyber attacks and data breaches has also increased, making it necessary for individuals, organizations, and governments to take steps to protect their systems and data. However, even with the best security measures in place, the human factor remains a critical vulnerability in cyber security.
One of the primary challenges with the human factor is that humans are prone to making mistakes, being complacent, and being unaware of the risks they face. Cyber criminals often exploit these weaknesses to gain unauthorized access to systems and data. For example, they may use social engineering tactics to trick users into divulging sensitive information or to install malware on their devices. In some cases, insiders may intentionally or unintentionally compromise security by, for example, sharing passwords, misusing access privileges, or failing to follow security protocols.
Another challenge is the growing complexity of technology and the lack of cybersecurity awareness among users. As technology advances and new devices, applications, and services emerge, the attack surface increases, and new vulnerabilities are exposed. Many users lack the technical expertise and knowledge necessary to protect themselves effectively, making them easy targets for cyber criminals.
To address the missing link between cyber security provision and the human factor, several steps can be taken. One approach is to improve cybersecurity awareness and education for all users. This can include providing training and resources to help individuals recognize potential threats and understand how to protect themselves and their data. Cybersecurity awareness can also be integrated into the culture of an organization, making security a shared responsibility among all employees.
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Cyberthreats are more imminent and dangerous than ever in terms of their persistence and severity. The volume keeps growing too, with the number of breaches rising 15.1% from 2020 to 2021. The costs per breach are also increasing–jumping 24.5% during the same period. A recent ThoughtLab study revealed the top cybersecurity challenges that are top-of-mind for IT leaders.
sources:
1) Automation answers for cybersecurity challenges - ServiceNow
2) New Special Issue Title Cyber Security for IT Systems, in Journal - pplied Sciences (ISSN 2076-3417)
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I believe parallel processing and AI (specifically Neural Networks) offer the best hope out of the currently available tech. Parallelism because of hardware processing speed (for formulation of a Response) and shear traffic volume considerations (Denial of Service attacks overwhelming critical points in our environment) all while remaining in the Cost envelope we can support, and Neural Networks again because of speed of adaptation to new attack structures. It seems to me that if you consider this with a game theory view, our current solutions to the problem of defense is we have what I have been calling Negative Leverage. (What I mean by that is we are trying to defeat an attack with only our own resources. The opponent can be anything from a Country (with almost unlimited resources) or multiple entities [Bots] only limited by the number of internet addresses), or combinations of both. Even the most simplistic analysis of this "game" will tell you that we can not win! After thinking about it, I have come to the realization that we must change the rules and/or applicable laws if we are to be successful.
My suggestion is to address to issue of responsibility. Currently, any one from a bored child on his home computer, to a state sponsored entity, can attack any know internet address, with no consequences in most cases. What I think is the answer to this issue is to introduce a "fear factor" to deter attacks. Under current US law (what I am most familiar with) it is illegal to insert code onto a computer you do not own (or are authorized to use). This has resulted in protecting attackers from an offensive response to date.
I would suggest a "passive aggressive" response to avoid breaking the law. (The insertion of responsive code is avoided by the attacker downloading the code embedded in his target data. This code could be concealed by Steganography tech and kept inactive/passive while still on the defended site using a variation of a "poison pill". [Becomes active if does not receive a "sleep" message" as scheduled.] This has the further advantage from the defender point of view, that if the embedded code stays inactive past the attacker's backup cycle [or better yet, cycles] it can result in significant resources being consumed to sanitize their environment. Thus achieving the goal of making the data theft not worth the trouble.
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Respected all Professors,
I would like to know what could possibly be the best book on research methodology for emerging technologies, or if anyone can help me determine what should be the least learning outcome for the student after studying the research methodology in the computer science domain. From what I've seen, a lot of students don't learn much in the research methodology class.
I would need some good suggestions and help.
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Thank you, Dr.Gupta and Dr. Jim. I truly appreciate your insightful suggestions.
I do agree with both of you.
However, I intend for our RM students to become legitimate researchers as opposed to the bogus mindsets of researchers...
Kindly share what could be the best RM books, so that I may study them also.
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HI,
I'm a master degree student intresested in Access Control Management and Data Protection. Any good topic suggestion and material will be apprciaated.
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Moses IGWEH ONYEKA Ilekenri Try to check perspectives of our following review papers:
1. Benbouzid, M.; Berghout, T. Quo Vadis Machine Learning-Based Systems Condition Prognosis?—A Perspective. Electronics 2023, 12, 527, doi:10.3390/electronics12030527.
2. Berghout, T.; Benbouzid, M.; Bentrcia, T.; Lim, W.H.; Amirat, Y. Federated learning for condition monitoring of industrial processes : A review on fault diagnosis methods , challenges , and prospects. Electronics 2022, 10, 158, doi:https://doi.org/10.3390/electronics12010158.
3. Berghout, T.; Benbouzid, M.; Muyeen, S.M. Machine Learning for Cybersecurity in Smart Grids: A Comprehensive Review-based Study on Methods, Solutions, and Prospects. Int. J. Crit. Infrastruct. Prot. 2022, 38, 100547, doi:10.1016/j.ijcip.2022.100547.
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I'm student in master degree in network and cybersecurity, and I have a project when I must to create a AAA architecture. But the teacher don't want we use RADIUS (because too easy :P ).
You have access to several files :
  • error_message.txt : where you can found the complete error message
  • shema.txt : the schema of network in ASCII format
  • conf_freediamter.txt : The configuration file of freediameter
  • conf_NAS.txt : The config file of CISCO switch 2960 which is the NAS
If you have need to another information, don't hesitate to ask me!
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Thank you for your answer !
But when I have re-run the network, I have noticed a little error when I have mask the real domain name. So we do have :
14:00:18 DBG [auth.rgwx] Received Diameter answer with error code '3002' from server 'aaa.galetteSaucisse.fr', session 192.168.1.2;1667653215;1;cookiehacker;aaa.galetteSaucisse.fr, translating into Access-Reject
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kindly tell me the scope of research in computer virus model with stochastic term and what are the limitation.
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The scope of research in computer virus models with stochastic terms is to understand how viruses spread and evolve over time.
By using stochastic models, the randomness and uncertainty that is inherent in the spread of these viruses can be anaylsed and determined.
In the past, research in this field focused primarily on developing mathematical models to describe the spread of viruses.
Currently, research in this field has become much more sophisticated. advanced statistical techniques and machine learning algorithms to better understand the spread and evolution of viruses. These newer models are able to account for the inherent randomness and uncertainty in the spread of viruses, and they are able to make more accurate predictions about the future.
The big Limitations are - these models are only as accurate as the data they are based on and they cannot account for all possible variables or unknowns.
Keeping up with advancements in technology and research, then incorporating them into models is a constant challenge.
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I am currently taking master's in computer engineering with specialization in Applied Cybersecurity. I have a hard time to come up with a thesis title. Would be very happy and grateful for all the suggestions. My study must have an integration of hardware and software. Thank you so much!
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The attached link looks into the US NIST framework as a way of minimizing cyber risk across a generic IT network and may assist your literature review.
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It has been more than half a century since the concept of Cybersecurity Operation Center (SOC) made its way into publication in 1980. Throughout these years there have been many developments in the computing sciences and other fields that use the same acronym SOC, and it may even be used in fields which will be invented in the future. The integration of Cybersecurity Operation Center to every sector is inevitable, and the usage of search engines will continue to grow. Therefore, for better communication and clarity among the internet community and for the search engines, it is recommendable to use CyOC instead of SOC as an acronym for Cybersecurity Operation Center.
What is your insight? Do we need to continue using SOC or update it to CyOC?
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Yes, Prof. Dr. Shafagat Mahmudova the use of CyOC has become prevalent in most developed countries.
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It is becoming obvious that social component is an important factor in dealing with threats and actual cyberattacks. Even the concept social engineering has acquired relevance among theoretical and practical approaches to cybersecurity. At the same time, social behaviour is strongly linked with individual psychological characteristics.
Do you consider Activity theory as a suitable theory for analyzing and studying attackers'/users' behaviour in cybersecurity research and application?
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@Rey Segundo Guerrero-Proenza. Thank you for your response. In my study I did not use the activity theory, instead constructivist approach is embedded in my theoretical framework of study wherein the dispersion of innovations theory is applicable. According to previous researchers, the constructivist inquiry is consistent with qualitative methods. And since my study is Qualitative research it aims to understand what people believe and feel as well as how they interpret events. You may read Rogers Dispersion and Diffusion Innovation Theory somehow might be related to your study. Thank you.
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I am looking forward to doing a research paper relevant to Cybersecurity for Digital Marketing for a coming up Research symposium in my Institution, please suggest me some topics relevant to it, Anyone interested can also join me as a Co-Author
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I believe you can start working on "Online Financial Security & Safe Shopping". I hope this would help and interested also for more collaboration.
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How can Big Data optimize urban planning and design?
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ان اتجاه التنمية للمدن المستقبلية من منظور الابتكار العلمي والتكنولوجي، يشير الى أن الطاقة النظيفة ستصبح الشكل الرئيس للطاقة الحضرية، واعتماد إعادة التدوير والاستخدام الفعال للموارد الاتجاه الرئيس للإنتاج الحضري
ودمج معلومات الخدمة العامة بشكل عضوي مثل النقل والاتصالات والموارد ومصادر الطاقة في المدينة، وابتكار أدوات وأفكار عمرانية جديدة للتصميم الحضري
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With most enterprise resources being hosted on the cloud, users tend not to be very aware of online security etiquette. This leaves the organisation's resources vulnerable to adversary attacks.
Who should be responsible for the total all-round awareness, implementation and enforcement of resource security? Is it the service providers, the clients or both?
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Well depicted Len Leonid Mizrah thank you.
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I am looking for research results on why cybersecurity implementations fail within an operational technology or cyber physical system environment. There are a lot of companies highlighting "problems" with implementations and promoting how their solutions would solve these problems, but I would like to know whether there is credible research available on this issue.
Thank you
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There will never be an end to cyber attacks and cyber security as long as cybersecurity is reactive and cyber attackers proactive. Since they are always a few steps ahead. They create viruses. They come up with solutions. As long as there is more than one user of the security system, it will always be vulnerable to cyber attacks.
There Gartner et al: (2020) Secure Access Service Edge (SASE) Forecast, confirms that SASE may be the answer. Introduced by Gartner, Secure Access Service Edge (SASE) is a forward-thinking framework in which networking and security functions converge into a single integrated service that works at the cloud edge to deliver performance and protection. Their statement answers the critical question on How can you ensure efficient and secure network access as your business evolves?
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During my research, I have dilemma on how to present the research methodology. I intend to study noise minimization schemes namely linearization, key switching, modulus switching and bootstrapping. Need suggestions.
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Dear Manoj Pokharel,
The following data may be helpful:
Homomorphic encryption is the Swiss army of cryptography. It allows to perform computations on encrypted data. this conjecture of processing on encrypted data was stated by [RAD78]. Until Gentry breakthrough in 2009, only partial solutions were offered. They deal with encrypted data with bounded functions in operations. Gentry raised the bound of number of operations by introducing a new method called the bootstrapping. This method allows to reduce the noise in the ciphertext and to perform more computing on it. Since Gentry's breakthrough, several improvements and several alternatives to the bootstrapping technique have been proposed to improve execution time and reduce resource consumption. This article studies the growth of noise and the noise management strategy in homomorphic encryption. It also presents the trend of hoping strategy from 2009 to 2016. Through the DGHV, it shows the management of noise on a one-bit encrypted message.
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A Regev-Type Fully Homomorphic Encryption Scheme Using Modulus Switching
A critical challenge in a fully homomorphic encryption (FHE) scheme is to manage noise. Modulus switching technique is currently the most efficient noise management technique. When using the modulus switching technique to design and implement a FHE scheme, how to choose concrete parameters is an important step, but to our best knowledge, this step has drawn very little attention to the existing FHE researches in the literature. The contributions of this paper are twofold. On one hand, we propose a function of the lower bound of dimension value in the switching techniques depending on the LWE specific security levels. On the other hand, as a case study, we modify the Brakerski FHE scheme (in Crypto 2012) by using the modulus switching technique. We recommend concrete parameter values of our proposed scheme and provide security analysis. Our result shows that the modified FHE scheme is more efficient than the original Brakerski scheme in the same security level.
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The Brakerski-Gentry-Vaikuntanathan (BGV) and Brakerski/ Fan-Vercauteren (BFV) schemes are the two main homomorphic encryption (HE) schemes to perform exact computations over finite fields and integers. Although the schemes work with the same plaintext space, there are significant differences in their noise management, algorithms for the core homomorphic multiplication operation, message encoding, and practical usability. The main goal of our work is to revisit both schemes, focusing on closing the gap between the schemes by improving their noise growth, computational complexity of the core algorithms, and usability. The other goal of our work is to provide both theoretical and experimental performance comparison of BGV and BFV. More precisely, we propose an improved variant of BFV where the encryption operation is modified to significantly reduce the noise growth, which makes the BFV noise growth somewhat better than for BGV (in contrast to prior results showing that BGV has smaller noise growth for larger plaintext moduli). We also modify the homomorphic multiplication procedure, which is the main bottleneck in BFV, to reduce its algorithmic complexity. Our work introduces several other novel optimizations, including lazy scaling in BFV homomorphic multiplication and an improved BFV decryption procedure in the Residue Number System (RNS) representation. We also develop a usable variant of BGV as a more efficient alternative to BFV for common practical scenarios. We implement our improved variants of BFV and BGV in PALISADE and evaluate their experimental performance for several benchmark computations. The experimental results suggest that our BGV implementation is faster for intermediate and large plaintext moduli, which are often used in practical scenarios with ciphertext packing, while our BFV implementation is faster for small plaintext moduli.
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I am looking for a research topic for my Master's thesis in cybersecurity, kindly assist with possible issues and objectives.
Implementing machine learning in cybersecurity will be preferred.
Thank you all
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Dear Alsharif Hasan Mohamad Abu Rbeian,
As you are most interested in conducting research on the implementation of machine learning in cyber security, so I propose the following research topic for your thesis: Analysis of the technological and economic determinants of the feasibility and validity of implementing machine learning technologies to improve cyber security systems and instruments. Within the framework of this topic, you can consider the possibilities of combining machine learning technology with other Industry 4.0 technologies to increase the possibility of improving cyber security systems and instruments. This topic may refer to improved cyber-security systems and instruments in public institutions, commercially operating non-financial business entities, or in banks and other financial institutions offering their services and financial products via the Internet, i.e., through online and mobile banking. An important element of the aforementioned issue of improving cyber-security systems and instruments through the use of machine learning and possibly other Industry 4.0 technologies is improving the performance of spam filters, whose task is to catch fake e-mails falling into e-mail inboxes containing hidden malware, ransomware viruses, viral Trojan horses, etc.
Best wishes,
Dariusz Prokopowicz
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please guide me about my MS research?how i work start from start
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Dear Wasim Wahid,
For the MA thesis, I suggest the following research topics on cyber security:
1. improving cyber-security techniques to protect the institution's/company's/enterprise's information systems from external cyber-attacks via email and ransomware viruses.
2. improving cybercrime risk management systems for integrated internal information systems connected to the Internet.
3. perfecting cyber security techniques implemented through the implementation of new Industry 4.0 technologies, including artificial intelligence, machine learning, Blockchain, multi-criteria simulation models, etc., and advanced analytical techniques such as Big Data Analytics, Business Intelligence.
4. improving the anti-spam systems used to protect email inboxes in order to increase the filtering out of emails where cybercriminals are using phishing techniques and/or sending malware and ransomware viruses in the background of the email.
5. improving cyber-security techniques for online and mobile banking systems, with a particular focus on mobile banking implemented on the bank's customer side using smartphones equipped with specific operating systems and web applications.
Regards,
Dariusz Prokopowicz
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what is the scope of research in the field of mathematical biology or mathematical model
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kindly send me the scope of research of computer virus propagation with stochastic term
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It's a clustering of cybersecurity dataset containing 10000 samples. The elbow plot shows 4 optimal cluster. Can I consider this Silhouette Score as indication of a good K-medoids clustering?
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Thanks for your response.
I've found in some articles that silhouette analysis or score is also used for cluster evaluation or validation, I used elbow method to find the optimal number of clusters and then trying to evaluate with silhouette analysis or score. But I don't understand if my clustering is good enough or not according to this silhouette score.
I have attached the elbow & silhouette plot.
Please suggest me some cluster evaluation methods.
Regards,
Nurun
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Hello,
I am developing a training framework for cyber security. I proposed curriculum based on my observations and discussions. Now, I would like to verify its effectiveness using a scientific method. Your comments and recommendations would be much appreciated. Thank you in advance.
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I think that having indicators such as "the level of interest and overall mastery of the content", "the ability to express opinions in the group", "having the spirit of criticism and criticism", "speaking well", the ability to analyze the topic, "how to express "Opinions" and other indicators that you can define can help in the evaluation.
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The paper The Limitations of Deep Learning in Adversarial Settings explores how neural networks might be corrupted by an attacker who can manipulate the data set that the neural network trains with. The authors experiment with a neural network meant to read handwritten digits, undermining its reading ability by distorting the samples of handwritten digits that the neural network is trained with.
I'm concerned that malicious actors might try hacking AI. For example
  • Fooling autonomous vehicles to misinterpret stop signs vs. speed limit.
  • Bypassing facial recognition, such as the ones for ATM.
  • Bypassing spam filters.
  • Fooling sentiment analysis of movie reviews, hotels, etc.
  • Bypassing anomaly detection engines.
  • Faking voice commands.
  • Misclassifying machine learning based-medical predictions.
What adversarial effect could disrupt the world? How we can prevent it?
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It is partly for this reason that AI development should be in a sandbox environment. Read more about using the sandbox approach: https://www.cambridge.org/core/journals/european-journal-of-risk-regulation/article/sandbox-approach-to-regulating-highrisk-artificial-intelligence-applications/C350EADFB379465E7F4A95B973A4977D
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I am working on cyber security domain. Currently, the work is to trace out the real ip address behind the VPN.
1. Is there any technique or api to trace out the real ip address which is masked with VPN?
2. How to determine whether the input ip address is original/proxy/vpn without any api support?
Researchers, Kindly provide the suggestion for the above questions.
#cybersecurity #vpn #proxy #networksecurity #researcher
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I'm thinking about doing academic project on Intrusion detection and prevention system on cloud computing. I also want to know how it can be successfuly conducted. Anyone having ideas on this topic would be much appreciated.
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All we know that, Cloud infrastructures are the subject of the cybersecurity attack for the upcoming digital world known as Economic Denial of Sustainability (EDoS). On the other hand DDoS attacks are made possible by massive botnets for the unavailable or down of the server. Since both EDoS and DDoS are performed in the same way.
So what are the differences between EDoS and DDoS?
How can we detect the EDoS attack in the cloud?
And what are the possible solutions for the prevention of EDoS attacks?