Cybersecurity - Science topic

Thank you for this timely and insightful post—it strikes at the core of what remains a significant blind spot in executive leadership. Cyber risk today is not just an IT issue but a strategic business risk that affects brand reputation, customer trust, and financial viability. Yet many executives still approach it with a compliance mindset rather than a resilience mindset.

Executives must begin seeing cybersecurity as a strategic differentiator—an enabler of innovation, digital transformation, and customer confidence. This requires integrating cybersecurity into enterprise risk management (ERM) frameworks and board-level discussions, not relegating it to IT silos. Demonstrating how robust cyber resilience supports business continuity and competitive advantage is key.

Some of the biggest barriers include:

CISOs and CROs must establish shared metrics and language, aligning cyber threats with business impacts. A practical step is to co-create risk heatmaps and scenario-based simulations, ensuring that cybersecurity priorities reflect enterprise risk appetite. Embedding cyber risk considerations into strategic planning cycles is essential.

Regulations like GDPR, NIS2, and DORA are raising the bar, but there's a danger of “checklist compliance.” True value comes when organizations go beyond the baseline—adopting a continuous assurance model that aligns with both regulatory and strategic goals.

I applaud your framing of the 4Vs and the Cybersecurity Compass. The next step is embedding this thinking into boardroom accountability and organizational culture. Let’s keep pushing the conversation toward strategic, proactive, and integrated cyber risk governance. Looking forward to others’ thoughts as well.

AI can play a big role in strengthening cybersecurity in schools and universities by spotting threats faster than traditional systems. It can detect unusual patterns like suspicious logins or data access and flag them in real time. But as we bring AI into these environments, we need to be careful with how it handles personal data and ensure it doesn’t introduce bias.

To protect privacy, we can use techniques like federated learning, which lets AI learn without pulling sensitive student data into one place. And to avoid bias, it’s key to train models on diverse data and constantly check for unfair outcomes especially in areas like access control or disciplinary monitoring.

Bottom line: AI can make education systems safer, but only if it’s built and used responsibly with privacy, fairness, and transparency in mind.

An effective communication strategy after a cyberattack can help a company position itself as a victim – if the strategy includes a commitment to affected consumers and employees...

https://theconversation.com/cyberattacks-how-companies-can-communicate-effectively-after-being-hit-255061?

Dear Rey Segundo Guerrero-Proenza ,

Artificial intelligence (AI) is changing and evolving the world in every domain from healthcare to transportation. One of the major components of AI in modern technology is being able to learn, evolve, adapt, and perform challenging tasks which are difficult for humans.

For students aspiring to study engineering, AI is not just for understanding but a necessity.

As future innovators and developers, one can truly use the potential artificial intelligence holds to build more efficient systems.

Please, look URL: https://blog.kalvium.com/ai-and-cybersecurity/

Regards,

Shafagat

I am also interested.

Seems interesting

Dear Rey Segundo Guerrero-Proenza ,

In cybersecurity, various AI types, such as machine learning, neural networks, and natural language processing, detect threats and automate responses.

https://www.balbix.com/insights/artificial-intelligence-in-cybersecurity/#:~:text=In%20cybersecurity%2C%20various%20AI%20types,detect%20threats%20and%20automate%20responses.

Regards,

Shafagat

Cybersecurity is a very broad field, covering many aspects of technical solutions, legal regulations and user attitudes. There are too many universities offering studies in cybersecurity and many of them are unable to provide an adequate level of education. I have noticed that most of them focus on the issue of legal regulations, which seems to me to result from the fact that it is easier to prepare a curriculum and educational materials. Therefore, I suggest you pay attention to this and try to maintain a balance between regulations and technical issues. Pay attention to the specifics of the university and the field of study, as well as your own interests and competences. I think that if you have experience in cryptography, it is worth making it an asset to your classes. On the other hand, it is always better to leave the issues you do not know about to specialists in these fields. Simply use the search engine as if you were looking for your dream course. Examples of good studies can be valuable inspiration, but poor examples also have their value because they will show you what can be done better or what mistakes to avoid. So first, check as many existing options as possible, and then go back to the ones that interest you the most. It's hard for me to give specific examples, because cybersecurity is a really big topic. I believe that education should be as practical as possible - but that can't mean limiting it to "training monkeys" - The practical aspect, as I understand it, must include appropriate theoretical preparation and an appropriate level of understanding, allowing for the practical application of knowledge and independence in performing tasks and diagnosing and solving problems. Some training courses lean too much towards the "practical" side, limiting exercises to mindless repetition of activities after the instructor, without proper understanding of what these activities consist of and what they are for. The impracticality of completely eliminating theoretical issues is best demonstrated by how people trained in this way do not cope in the real work environment. Do you think you need to adapt the curriculum to the gender of students? The fact is that some interests are quite strongly correlated with gender, but I think that the selection for these interests takes place at the stage of choosing the field of study. That's why in some fields we can encounter a large gender gap, even at the level of 80:20 or 90:10. But once you have a group selected based on interests, I don't expect gender to be a significant factor. So don't focus on adapting the program to a specific group, but try to make the classes attractive to a student interested in cybersecurity. It doesn't matter whether the student is a man or a woman, just as it doesn't matter whether they listen to rap or punk rock.

The work presents a comprehensive and insightful analysis of the significant negative externalities resulting from cybersecurity non-compliance, effectively linking microeconomic organizational decisions to macroeconomic impacts. By employing a mixed-methods approach and integrating behavioral economics, the paper introduces innovative concepts such as the Behavioral Compliance Index (BCI) and dynamic penalty structures, which offer actionable strategies for mitigating compliance gaps. The use of case studies, particularly the Bayview Asset Management settlement, adds empirical weight to the arguments presented. Overall, this work contributes valuable perspectives to ongoing discussions surrounding cybersecurity policy, particularly in relation to the EU NIS 2 Directive and U.S. CIRCIA legislation, highlighting the need for adaptive regulatory frameworks that align with organizational behavior and systemic risk management.

yes

I am currently doing research on university policies on the use of IA and I am very surprised that many do not stress to their students that the information one places in the LLM is, many times, for them. You should focus on internalizing taking care of personal or team data where you work.

Greets

Thank you, Anatol Badach , Daniyel Yaacov Bilar & Wasswa Shafik for your insightful suggestions.

Appreciated.

Dear Dariusz Prokopowicz ,

Analytics have long been used for commercial purposes, where the return on investment is easier to see and justify. Now the ISF is urging business to use the same concepts to secure its networks. “Few organizations currently recognize the benefits for information security, yet many are already using data analytics to support their core business,” says Michael de Crespigny, CEO at ISF. “With the speed and complexity of the threat landscape constantly evolving and the prevalence of combined threats, organizations need to start moving away from being retrospective and reactive to being proactive and preventative.”

The digital age is awash in data. The amount of information organizations collect is growing exponentially, creating a vast data repository with immense potential. This data goldmine holds the key to not only understanding customers but also fortifying security.

Regards,

Shafagat

This is good work Shafagat Mahmudova . Have you tested using EEG head sets in your lab environment?

If your organization is having Adaptive risk authentication product integrated into your IAM system, then network anomaly will be available out-of-the-box. There are certain predictors available by default, such as Anonymous Network or Allowed Networks etc., where you can provide whitelisted network IP ranges for detecting the requests from desired traffic. In this case, it is recommended to enable risk protection in learning mode or auditing mode for couple of weeks until it generates good amount of metadata for risk analysis.

On the other hand, if you don't have risk authentication product in your organization, you can leverage RNNs (Recurrent neural networks and Decision Trees), clustering algorithms (K-Means and DBSCAN which is Density based spatial clustering of applications with noise). These two methods forms the basis of AI-powered detection techniques. Try products available in the market DarkTrace Enterprise Immune System for Threat detection with AI-powered platform.. For SOAR security orchestration Automation & Response, try Palo Alto Cortext XSOAR platform.

I prefer the former approach where AI/ML is already built-in if you wanted to implement threat detection and prevention in shorter time frame. I prefer the latter approach if you need more flexibility with multiple AI-models rather and you are not time bound for project.

Hope this helps.

Thank you

Mahendra

Integrating AI into cybersecurity systems presents several significant challenges, including the need for high-quality, large-scale data to train accurate models, which is often difficult to obtain due to privacy concerns and the rarity of certain attacks. Adversarial attacks, where attackers manipulate AI inputs to deceive systems, further complicate reliability. The complexity and "black-box" nature of many AI models hinder interpretability, making it hard to trust or debug their decisions. Additionally, integrating AI with legacy systems, ensuring scalability, and keeping up with the dynamic threat landscape are resource-intensive tasks. Regulatory compliance, ethical considerations, and the high costs of development and maintenance also pose barriers. Finally, the shortage of skilled professionals who understand both AI and cybersecurity slows adoption, while issues like false positives and negatives can undermine system effectiveness and lead to alert fatigue. Addressing these challenges requires ongoing research, collaboration, and investment in both technology and talent.

Dear Fábio Lopes Brezolin ,

Dear Shahnawaz Mohammed,

I have been conducting research in this area for years. Among other things, the following conclusions have emerged from my research:

Artificial intelligence and quantum computing are technologies that have great potential to improve cyber security. Their ability to automatically detect threats, create unbreakable ciphers and analyse huge data sets at unprecedented speed opens up new possibilities in the fight against cybercrime. As these technologies continue to evolve, their role in protecting our data and systems can be expected to grow, providing us with greater security in the digital age. AI is playing an increasingly important role in cyber security, acting as an automated sentinel that constantly monitors and analyses vast amounts of data to identify any abnormal behaviour that may indicate a cyber attack. Thanks to advanced algorithms, AI is able to detect threats quickly and effectively, responding to them in real time, minimising potential damage. What's more, machine learning models, an integral part of AI, have the ability to predict cyber threats before they happen, allowing systems to proactively strengthen resilience and prevent attacks. Quantum computing, in turn, is bringing about a revolution in data encryption. Quantum key distribution (QKD) is an innovative technique that uses the principles of quantum physics to create virtually unbreakable ciphers, securing communication channels against potential attacks. In addition, quantum computers, thanks to their immense computing power, can be used to accelerate complex operations such as the analysis of large data sets, significantly strengthening security systems, making them more effective and resilient to attacks. The combination of artificial intelligence and quantum computing opens up new possibilities in cyber security. For example, Quantum Machine Learning (QML), which is machine learning assisted by quantum computers, makes it possible to process and analyse huge amounts of data at speeds unattainable by classical computers. This, in turn, allows for even faster and more accurate threat detection, as well as the creation of more advanced predictive models that can predict attacks with greater precision. In summary, artificial intelligence and quantum computing are technologies that have great potential to improve cyber security. Their ability to automatically detect threats, create unbreakable ciphers and analyse huge data sets at unprecedented speed opens up new possibilities in the fight against cybercrime. As these technologies continue to evolve, their role in protecting our data and systems can be expected to grow, providing us with greater security in the digital age.

I conduct research in the problems of analysing cybercrime attacks carried out using ransomware viruses and in improving cyber security systems. The conclusions of my research are included in the following articles:

Development of malware ransomware as a new dimension of cybercrime taking control of IT enterprise and banking systems

Determinants of the development of cyber-attacks on IT systems of companies and individual clients in financial institutions

Cybersecurity of Business Intelligence Analytics Based on the Processing of Large Sets of Information with the Use of Sentiment Analysis and Big Data

Thank you very much,

Best regards,

I would like to invite you to scientific cooperation,

Dariusz Prokopowicz

The increasing development of large language models could be an enormous game-changer in cybersecurity by simultaneously making it easier and harder. On one hand, this technology will make the detection of threats automatic, scan vulnerabilities, and prevent phishing, improving the response times and accuracy of those responses. Yet with them also come new risks that malicious actors could use to craft convincing phishing emails, create complex social engineering attacks, or automatically generate malware. As LLMs become increasingly available, the ability of cybersecurity professionals to evolve rapidly will mean making full use of advanced AI models in a way that effectively counters emerging threats.@Bikash Saha

Basic constituents of quantum computing are qubit, superposition, and entanglement. Then, the major application areas are cryptography, AI, drug discovery, and optimization. Utilize Google Scholar, arXiv, and IEEE Xplore to find academic literature on quantum computing. Industry leaders to follow include IBM Quantum and Google Quantum AI. Research can also be drawn from research papers, government reports, and quantum computing conferences. Hands-on experience with platforms such as IBM Quantum Experience and Microsoft Q# will give a profound insight. Keep up to date with the latest development about quantum computing through blogs, forums, and communities.@Juan Aranda

Initially, my idea was to combine two tools to assess students' knowledge of cybersecurity and integrate the Item Response Theory (IRT) into the system. However, at the moment, I am exclusively focused on understanding how to implement this model specifically in Juice Shop. The goal is to use IRT to more accurately measure users' level of knowledge, adjusting the difficulty of questions and challenges based on their performance.

AI can significantly boost cybersecurity by automating threat detection, accelerating incident response, and identifying potential vulnerabilities. However, it also carries a risk of misuse by cybercriminals. To address this, organizations should establish robust governance frameworks, strictly regulate access to offensive AI capabilities, and create transparent methods to trace harmful outputs. These measures ensure that AI is deployed defensively while its misuse is restrained through oversight, accountability, and technical safeguards. In parallel, collaboration among industry, government, and academia bolsters shared threat intelligence and standardizes best practices, while secure-by-design models and adversarial testing fortify AI systems themselves. Finally, with ethical guidelines, regulatory oversight, and ongoing skill development, AI can serve as a powerful shield against cyber threats without inadvertently empowering attackers.

The rise of deepfakes, which use artificial intelligence to create realistic but fake audio, video, or images, poses a serious threat to trust in digital systems and processes within Industry 4.0 and 5.0. These technologies can be used to spread misinformation, manipulate public opinion, or commit fraud, making it difficult to trust what we see or hear online. In industries where data and automation are central, deepfakes can disrupt operations, such as by impersonating executives to authorize fake transactions or manipulating sensor data in smart systems. This undermines the reliability of digital platforms and creates security risks. To counter this, organizations must invest in technologies to detect deepfakes and strengthen digital trust through robust cybersecurity measures and ethical AI practices.

Thanks for this information. The deadline is not stated.

Hello, I am interested in that job. Can you please connect me to it?

The 4 Vs of cyber risks refer to Volatility, Visibility, Vulnerability, and Value. Managing these risks effectively requires a combination of strategies and frameworks that address each of these aspects. Here are some effective strategies and frameworks for managing the 4 Vs of cyber risks:

1. Risk Management Frameworks: Organizations can adopt risk management frameworks such as NIST Cybersecurity Framework (CSF), ISO/IEC 27001, or COBIT to provide a structured approach to managing cyber risks. These frameworks offer guidelines and best practices for identifying, assessing, and mitigating risks.

2. Continuous Monitoring: Implementing continuous monitoring solutions can help organizations enhance real-time risk visibility. This involves monitoring network traffic, system logs, and other relevant data to detect potential security breaches or anomalies.

3. Threat Intelligence: Organizations can leverage threat intelligence feeds to stay informed about the latest threats and vulnerabilities. This information can be used to update security controls and improve incident response capabilities.

4. Vulnerability Management: Regularly scanning systems and networks for vulnerabilities, and promptly addressing identified vulnerabilities, can help reduce the attack surface and minimize the impact of potential breaches.

5. Security Automation: Automating repetitive tasks such as log analysis, threat detection, and incident response can help organizations improve their adaptability and responsiveness to cyber threats.

6. Security Governance: Establishing a strong security governance framework can help ensure that security policies, procedures, and standards are effectively implemented and maintained across the organization.

7. Employee Education and Awareness: Educating and training employees on cybersecurity best practices can help prevent human-error-induced breaches. Regularly conducting security awareness training can foster a culture of security within the organization.

8. Collaboration and Communication: Encouraging collaboration and communication between different departments, such as IT, security, and business units, can help improve risk visibility and facilitate a more comprehensive understanding of cyber risks.

9. Technology Integration: Implementing a holistic cybersecurity solution that integrates various security tools and technologies can help organizations streamline their security operations and improve their overall cybersecurity posture.

People, processes, and technology all play crucial roles in creating a robust approach to continuous cyber risk assessment. Here's how each component contributes:

1. People: Skilled and knowledgeable security professionals are essential for developing and implementing effective cybersecurity strategies. They should stay updated with the latest threats, vulnerabilities, and best practices, and be able to adapt quickly to evolving cyber threats.

2. Processes: Well-defined security processes and procedures help ensure that cybersecurity measures are consistently applied across the organization. This includes incident response plans, vulnerability management processes, and regular security audits.

3. Technology: Advanced cybersecurity technologies, such as intrusion detection systems, security information and event management (SIEM) solutions, and encryption, can help organizations detect, prevent, and respond to cyber threats more effectively.

By combining these elements, organizations can create a robust approach to continuous cyber risk assessment that enhances real-time risk visibility, prioritization, and adaptability.

I have experience in radar infrastructure and cybersecurity requirements during the deployment of 5G spectrum. Also law enforcement applications of AI.

Quite interesting. I can always be your research partner in areas of fraud detection and MFA

Oh my everyone is so much more experienced then me here. 😅 I am on my senior majoring in cybersecurity investigations and enforcement. I thought this would be a good place to start networking and learning from the experts :)

A Man-in-the-Middle (MitM) attack in the context of Transport Layer Security (TLS) exploits the communication between two parties, such as a client (eg, a browser) and a server (eg, a website), to intercept, decrypt, or data in transit. TLS is designed to secure such communications, but attackers may exploit vulnerabilities in implementation, configuration, manipulation, or trust mechanisms to bypass it.

Steps in a MitM Attack on TLS :

Mechanisms to Mitigate MitM Attacks on TLS :

Greetings You may check

Under research items there are several articles on computing Download them and see if they r relevant to your research domain

Interested.

For a software engineer, generative AI can assist with code generation. GitHub Copilot and ChatGPT are great tools for speeding up development. Another use case is bug detection and fixing. AI can analyze codes and identify and fix bugs. In other words, generative AI can be a personal assistance for software developers.

Supply chains are as much affected by cyber risks as the primary organization. Even within the IT department, the supply chain faces all-pervasive cyber risks.

The same can be achieved by

1. Network Segmentation

2. Proper and Secure Network Design

3. Zero Trust Architecture

4. Use of Original Software in System

5. Regular Software Updates

6. Monitoring and Logging of System Log

7. Regular Security Audits of Network

8. Deployment of Antivirus system

9. Monitoring of Antivirus log

10. Incident Response Plan

11. Deployment of IDS and IPS

12. Advanced Threat Detection

13. Monitoring and Logging of IDS/IPS Log

14. Employee Training and Awareness

15. Implementation of Data Encryption

16. Multi-Factor Authentication Mechanism .

Dear Mustapha Anis Younes These articles can answer your question.

Dear Miguel Reyes ,

Dear Haq Nawaz Malik Finding professors who are open to including enthusiastic students in their AI, ML, or Computer Science research can be a rewarding endeavor. Here are some steps you can take to identify and approach potential professors:

By actively seeking out professors and demonstrating your enthusiasm and willingness to learn, you can increase your chances of finding a research opportunity in AI, ML, or Computer Science.

For your master's research in cyber and network security, particularly with a focus on cryptography and encryption within a network security context, several promising topics align with your interests and your lab's needs. Given the emphasis on network security and the desire to explore areas related to zero trust security, here are some viable research directions.

One potential topic is the Implementation of Zero-Trust Security in Software-Defined Networks (SDN). This research could involve integrating cryptographic techniques within SDN architectures to enforce zero-trust principles. SDN allows centralized network traffic control, which can be leveraged to implement dynamic and context-aware security policies. By focusing on how cryptographic protocols can be adapted or designed to fit into SDN frameworks, you can explore how these networks can remain secure against internal and external threats without compromising performance.

Another area to explore is the development of Lightweight Encryption Protocols for IoT Devices in a Zero Trust Network. IoT devices often have limited processing power and energy resources, making traditional encryption methods impractical. Research in this area would involve designing encryption methods that are both secure and efficient enough for use in resource-constrained environments while also ensuring that these devices can operate securely within a zero-trust framework.

A third topic worth considering is Cryptographic Techniques for Enhancing Privacy in Zero-Trust Architectures. This could involve studying advanced cryptographic methods, such as homomorphic encryption or secure multi-party computation, and applying them within a zero-trust framework to ensure that data privacy is maintained even as data is shared across different parts of the network. This research would be particularly relevant as privacy concerns become increasingly significant in the design of secure systems.

Another relevant topic is Post-Quantum Cryptography in Zero-Trust Networks. With the advent of quantum computing, traditional cryptographic methods may soon become obsolete. This research would focus on integrating post-quantum cryptographic algorithms into zero-trust frameworks to future-proof networks against the impending threats posed by quantum computers.

You could research Zero Trust Access Control Using Attribute-Based Encryption (ABE) for a more access-control-focused approach. This would involve designing systems where access to resources is controlled by a user’s attributes, ensuring that even within a trusted network, users only have access to the resources necessary for their tasks. This would be particularly relevant in complex organizational networks where different users require different access levels.

Lastly, exploring End-to-End Encryption in Zero-Trust Network Architectures could be a promising direction. This research would focus on ensuring that data remains encrypted throughout its journey across the network, from the point of origin to its destination. This is crucial in zero-trust environments, where the assumption is that no part of the network can be trusted and that data must be protected at all times.

Each of these topics aligns with current trends in cybersecurity research and would allow you to contribute meaningful advancements to the field. By focusing on the intersection of cryptography, network security, and zero-trust principles, you can develop a research project that is both technically challenging and highly relevant to the needs of modern digital security environments. This approach also aligns with your cybersecurity and cybercrime analysis career goals, providing a solid foundation for your future professional endeavors.

Yes, it is feasible to use a survey like the Likert scale to study the efficiency of AI in cybersecurity, particularly as part of a broader quantitative research approach. The Likert scale, which measures respondents' attitudes or perceptions on a scale (e.g., from "strongly agree" to "strongly disagree"), can effectively capture subjective evaluations of AI's efficiency from professionals and users in the cybersecurity field. By designing a well-structured survey, researchers can gather data on various aspects of AI's performance, such as its accuracy, speed, reliability, and overall impact on cybersecurity operations. This method allows for quantifying perceptions and experiences, which can be statistically analyzed to identify trends, correlations, and potential areas for improvement. However, it’s important to complement the Likert scale with other data collection methods, such as expert interviews or case studies, to provide a more comprehensive understanding of AI's efficiency. Additionally, the survey questions must be carefully crafted to reflect the different dimensions of AI efficiency in cybersecurity accurately. While a Likert scale survey can provide valuable insights, it should be part of a mixed-methods approach to capture the complexity of AI's role in cybersecurity.

I am particular interestef in your research as it aligns with my current project on cyber security @Elshan Baghirov

Thank you, Karina Grinenko

I will look into it.

The total number of members in a vehicle platoon can significantly affect the overall cybersecurity of the platoon. As the number of vehicles in the platoon increases, the complexity of maintaining secure communication and ensuring overall cybersecurity also increases. Here are some key considerations:

### 1. **Increased Attack Surface**:

- **More Entry Points**: Each additional vehicle adds an entry point for potential cyberattacks. Attackers might exploit vulnerabilities in one vehicle's communication system, sensors, or control units, which could compromise the entire platoon.

- **Network Complexity**: With more vehicles, the communication network becomes more complex. Managing secure data transmission among a larger number of vehicles increases the risk of data breaches, man-in-the-middle attacks, or jamming.

### 2. **Inter-Vehicle Communication (V2V)**:

- **Bandwidth and Latency**: A larger platoon requires more data to be exchanged between vehicles. This can strain the network, potentially leading to delays in communication, which attackers might exploit. Securing the communication channel against eavesdropping or spoofing becomes more challenging as the number of vehicles increases.

- **Synchronization and Consistency**: Ensuring that all vehicles in a large platoon receive and process data simultaneously and consistently is difficult. Any lag or inconsistency can be exploited by attackers to create confusion or to insert malicious commands.

### 3. **Consensus and Decision-Making**:

- **Complexity in Consensus Algorithms**: In larger platoons, decision-making (such as adjusting speed or direction) must account for more vehicles, making the consensus algorithms more complex. This complexity can lead to vulnerabilities if the algorithms are not properly secured.

- **Potential for Misinformation**: In a large platoon, if an attacker compromises one vehicle, they could inject false information into the network, leading to incorrect decisions by other vehicles. This could result in collisions or unsafe maneuvers.

### 4. **Scalability of Security Protocols**:

- **Challenges in Encryption**: As the number of vehicles grows, the encryption and decryption processes for secure communication need to scale efficiently. More vehicles mean more keys and certificates to manage, which can increase the risk of key management failures or exploitation of weaker encryption protocols.

- **Authentication Overhead**: Ensuring that every vehicle in a large platoon is authenticated and trusted adds overhead to the system. The complexity of maintaining a robust authentication process can increase the risk of vulnerabilities.

### 5. **Impact of a Single Compromised Vehicle**:

- **Cascading Effects**: In larger platoons, the impact of a single compromised vehicle can be more severe. A hacked vehicle might send malicious commands or false data, affecting the behavior of other vehicles. The more vehicles there are, the more widespread the potential disruption.

- **Difficulty in Isolation**: Identifying and isolating a compromised vehicle in a large platoon is more challenging. The larger the platoon, the more difficult it becomes to quickly detect and mitigate the impact of a cybersecurity breach.

### 6. **Cooperative Adaptive Cruise Control (CACC)**:

- **Increased Dependency**: Larger platoons often rely on Cooperative Adaptive Cruise Control, where vehicles communicate to maintain speed and distance. If the cybersecurity of CACC is compromised, the entire platoon could be at risk. The complexity of securing CACC increases with the number of vehicles.

### 7. **Redundancy and Fault Tolerance**:

- **Need for Redundant Systems**: As the platoon grows, redundancy in communication and control systems becomes more important to ensure that the platoon can operate safely even if some vehicles are compromised. However, implementing and managing redundancy increases the system's complexity and potential points of failure.

- **Resilience to Attacks**: A larger platoon must be more resilient to attacks. Ensuring that the platoon can continue to function safely even if part of the system is under attack is critical, but more difficult to achieve as the number of vehicles increases.

### 8. **Human Factors and Response**:

- **Increased Coordination Challenges**: In larger platoons, coordinating human responses to cybersecurity threats becomes more difficult. Drivers or operators may have less control or understanding of the overall platoon’s state, complicating responses to potential attacks.

- **Training and Awareness**: Ensuring that all drivers or operators in a large platoon are adequately trained in cybersecurity practices is more challenging, leading to potential weaknesses in the human element of security.

In summary, as the number of vehicles in a platoon increases, so do the challenges in maintaining cybersecurity. The increased attack surface, complexity of communication, and need for robust authentication, encryption, and redundancy make it more difficult to secure the platoon against cyber threats. Addressing these challenges requires advanced security protocols, real-time monitoring, and resilient system designs.

SIEM systems can effectively handle and analyze unstructured data from IoT devices and social media by using advanced technologies such as AI and machine learning. These technologies allow SIEM systems to process large amounts of diverse data types, extracting valuable patterns and insights. For example, AI-based data analytics can consolidate and contextualize data, providing in-depth insights and coherent risk scores. Machine learning and deep learning algorithms can analyze behavior over time, identifying anomalies and potential threats more effectively.

Furthermore, integrating big data analytics tools with SIEM systems improves their ability to handle and interpret unstructured data. This enhancement leads to improved threat detection and response capabilities.

Organizations can effectively balance network accessibility and robust security by embracing a proactive risk management approach that prioritizes secure access pathways, continuous monitoring, and adaptive security frameworks. This includes implementing VPNs and secure access service edge (SASE) for remote workers, ensuring data encryption, and utilizing multi-factor authentication. Regularly updating security protocols and educating employees on best practices fosters a security-first culture. Innovative solutions like zero-trust architecture and AI-driven threat detection can further enhance security without compromising accessibility. By predicting and adapting to emerging threats, organizations can maintain a resilient and accessible network, even with limited remote work and cloud services, thus safeguarding their operations and corporate missions.

Generative Artificial Intelligence is a new field for me, but I have already formed an opinion that is based on reading the discussion thread question and the six responses. In my view, the "bug" in GAI is rooted, first, in the way GAI works like a "motion detector," by which I mean that its premise is that normal behavior is systematic or can be systematized. The assumption seems to be that everything should be operating like robots and anybody who steps out of line is held to be suspected of malfeasance. Second, the attackers have sophisticated means by which to mimic the normalized and systematized behavior perfectly and thereby conduct a peaceful invasion, undetected. This impression is admittedly skimming the surface of the discussion thread question; however, it just simplifies the gist of the previous answers to the discussion question.

Prior to this GAI issue in the context of detecting cybercrime attacks, I have typically encountered Generative Artificial Intelligence as a big problem in the college classroom. Students have begun to plagiarize by using prefabricated compositions and term papers available on the Internet. College instructors are strategizing to turn the tide by brain-storming and determining ways to train themselves and their students to use GAI skillfully and honestly.

The concept of compatibility may hold one of the keys to de-demonizing Generative Artificial Intelligence. Perhaps GAI will be more adaptable and useful in some contexts than in others. Fortunately, my direct experiences as a recipient of the benefits of GAI are very positive. For one thing, GAI saves a great deal of time by weeding out the spam in my email accounts. However, as the above answers indicate, there is no 100% guarantee that cybercriminals are not going to devise new ways to trick email recipients into falling into a skillfully camouflaged trap that will worsen exponentially.

Yes, that's right. Personalized agents generated from web-accessible intelligent chatbots equipped with generative artificial intelligence technology can be helpful in student learning if teachers properly guide the learning process in which students creatively and thoughtfully use these new technologies. Since the scale of cybercrime threats is steadily increasing and students' smartphones and laptops are also being infected by malware created by cybercriminals, it may be a good solution to develop personalized advisors based on intelligent chatbots equipped with generative artificial intelligence technology, who will creatively help identify potential cybercrime threats and advise students on how to behave in certain cybercrime attack situations. In this way, intelligent chatbots can support the education process and can be an important part of the cybercrime risk management process.

I described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

I would like to invite you to join me in scientific cooperation on this issue,

Warm greetings

Dariusz Prokopowicz

To model and simulate future cyber attacks in a predictive manner, various mathematical tools are employed in cyber security research. Some of these tools include:

1. *Graph Theory*: Modeling network topologies and analyzing connectivity to identify potential vulnerabilities and attack paths.

2. *Probability Theory*: Calculating likelihood of attacks and estimating risk using probability distributions (e.g., Bayesian networks).

3. *Game Theory*: Simulating attacker-defender interactions to predict strategic behaviors and optimize defense strategies.

4. *Differential Equations*: Modeling the spread of malware and cyber epidemics to predict and contain outbreaks.

5. *Machine Learning*: Training predictive models on historical data to identify patterns and forecast future attacks.

6. *Network Science*: Analyzing complex network structures to understand cascading effects of attacks and optimize network resilience.

7. *Optimization Techniques*: Finding optimal defense strategies using linear and nonlinear programming (e.g., integer programming).

8. *Stochastic Processes*: Modeling random events and uncertainty in cyber attacks to predict and prepare for potential scenarios.

9. *Information Theory*: Quantifying and analyzing information flows to detect and prevent data breaches.

10. *Simulation and Modeling*: Using tools like Petri Nets, System Dynamics, and Discrete Event Simulation to model and simulate various cyber attack scenarios.

These mathematical tools help researchers and security professionals proactively anticipate and prepare for potential cyber threats, enabling more effective defense strategies and incident response planning.

Dear Wyatt King,

In terms of my thesis topic on cyber security in finance, I propose to analyze the applicability of generative artificial intelligence technology in combination with Big Data Analytics and other Industry 4.0/5.0 technologies to improve online and mobile banking cyber security systems, instruments and applications.

Best wishes,

Dariusz Prokopowicz

I WOULD LIKE TO PARTICIPATE

I saw this article:

'How AI is improving climate forecasts'

Dear Rajni Garg ,Artificial Intelligence (AI) Tools in Scientific Research: Revolutionizing Discovery and Efficiency1. Scite Assistant 2. Consensus 3. Elicit 4. ChatGPT 5. ChatPDF 6. Research Rabbit 7. SciSpace https://www.ilovephd.com/top-7-artificial-intelligence-ai-tools-in-scientific-research/

Regards,

Shafagat

I'm looking for a team.

Dear Cristian Bucur ,

Thank you for reaching out and giving us the opportunity to contribute to your survey on drone cybersecurity. I appreciate your efforts in gathering insights on this crucial and evolving aspect of technology. Some references below will give you some insights we surveyed and summarized, including:

Hope it helps

Regards

I appreciate the valuable time you spent writing your thoughtful answers.

Rachid Ait Maalem Lahcen, You mentioned it correctly: "and convenient."

Yashar Salami, You mentioned correctly: "false positives or false negatives."

Indeed, biometrics is not a fallacy; biometrics bear recognizable features of human beings.

Biometrics has so many security-lowering features!

A probabilistic recognition process can NOT be used to yield a deterministic "yes/no" result like a text password/PIN.

Moreover, biometrics spoofs are so easy to make.

How can biometrics improve security?

With more than a decade of hands-on research with different biometrics modalities, I realized biometrics is convenient, but biometrics can lower security. Biometrics has inherent security-lowering problems!

Please read my brief review article, "Biometrics is Not a Fallacy, But Can Lower Security." https://www.linkedin.com/pulse/biometrics-fallacy-can-lower-security-debesh-choudhury-phd/

Thank you so much.