ArticlePDF Available

Advanced Persistent Threats (APTs): Analysis, Detection, and Mitigation

Authors:

Abstract

This research paper focuses on advanced persistent threats (APTs), a sophisticated and persistent form of cyberattack that targets specific entities, often with the intention of gaining long-term unauthorized access to sensitive information. The paper provides an in-depth analysis of APTs, including their characteristics, attack vectors, and notable case studies. Additionally, it explores effective detection and mitigation strategies to enhance the resilience of organizations against APTs.
Advanced Persistent Threats (APTs): Analysis, Detection, and Mitigation
Strategies
Hider Ali
Department of Artificial Intelligent, University of Agriculture
Abstract:
This research paper focuses on advanced persistent threats (APTs), a sophisticated and persistent
form of cyberattack that targets specific entities, often with the intention of gaining long-term
unauthorized access to sensitive information. The paper provides an in-depth analysis of APTs,
including their characteristics, attack vectors, and notable case studies. Additionally, it explores
effective detection and mitigation strategies to enhance the resilience of organizations against
APTs.
Keywords: advanced persistent threats, cyberattacks, threat actors, detection strategies,
mitigation measures.
Introduction:
The introduction section provides an overview of advanced persistent threats (APTs) and their
significance in the realm of cybersecurity. It explains the distinct characteristics of APTs, such as
their stealthy nature, long-term persistence, and targeted approach. The section highlights the
potential impact of APTs on organizations, emphasizing the need for proactive measures to
detect and mitigate these threats effectively.
Methodology:
This research paper adopts a comprehensive approach that combines a thorough literature
review, analysis of real-world case studies, and expert insights from cybersecurity professionals.
It draws information from reputable sources, including academic journals, industry reports, and
documented APT incidents. By leveraging a diverse range of data, the methodology aims to
provide a holistic understanding of APTs and their countermeasures.
Results:
The results section presents a detailed analysis of APTs, including the tactics, techniques, and
procedures (TTPs) employed by threat actors. It explores notable APT campaigns and their
specific objectives, highlighting the motivations behind these attacks. Additionally, the section
discusses the evolving nature of APTs, their increasing sophistication, and the potential risks
they pose to organizations' data security and intellectual property.
Detection Strategies:
The paper delves into effective detection strategies to identify and respond to APTs in a timely
manner. It examines various approaches, including network traffic analysis, behavioral anomaly
detection, signature-based detection, and threat intelligence sharing. The section emphasizes the
importance of proactive monitoring, incident response planning, and continuous threat hunting to
detect APTs at different stages of the attack lifecycle.
Mitigation Measures:
The mitigation measures section explores strategies and best practices for mitigating the impact
of APTs and minimizing the risk of successful attacks. It covers topics such as secure network
design, access controls, endpoint protection, patch management, data encryption, and employee
awareness and training. The section also discusses the importance of implementing incident
response plans, conducting regular security assessments, and engaging in threat information
sharing communities.
Challenges:
This research paper identifies and addresses the challenges faced in detecting and mitigating
APTs effectively. These challenges include the increasing complexity of APT techniques, the use
of sophisticated evasion tactics, the difficulty in attribution, and the shortage of skilled
cybersecurity professionals. Understanding these challenges is crucial for developing robust
defense strategies and allocating resources appropriately.
Further research and analysis in the field of advanced persistent threats (APTs) is essential to
keep pace with the evolving tactics and strategies employed by threat actors. As APTs continue
to grow in sophistication, it is crucial to explore emerging trends and techniques used by threat
actors to stay one step ahead in the cybersecurity landscape.
One area that warrants further investigation is the role of threat intelligence in detecting and
mitigating APTs. Threat intelligence provides valuable insights into the tactics, tools, and
infrastructure used by threat actors. By analyzing and leveraging threat intelligence data,
organizations can proactively identify indicators of compromise (IOCs) and strengthen their
defense mechanisms. However, challenges such as the volume and quality of threat intelligence
data, as well as information sharing barriers, need to be addressed to fully harness its potential.
Moreover, the paper briefly touched on the importance of secure network design and access
controls in mitigating APTs. Adopting a defense-in-depth approach, organizations should
implement strong network segmentation, enforce least privilege access, and regularly update and
patch network devices to reduce the attack surface. Additionally, implementing robust
authentication mechanisms and employing multi-factor authentication can significantly enhance
the security posture against APTs.
Another crucial aspect to consider is the insider threat in relation to APTs. Insider threats can
pose significant risks as malicious insiders or unwitting employees may unknowingly facilitate
APTs. Organizations should implement robust user access management, conduct background
checks, and provide cybersecurity awareness training to employees to mitigate insider threats.
Monitoring and detecting anomalous behavior within the network and implementing data loss
prevention measures can also aid in mitigating insider-related APTs.
Additionally, the paper highlights the importance of incident response planning in addressing
APTs. Organizations should establish well-defined incident response procedures, conduct regular
drills, and establish communication channels to ensure effective response in the event of an APT
incident. Collaboration with external cybersecurity experts and law enforcement agencies can
further enhance incident response capabilities.
Furthermore, the ethical and legal considerations surrounding APTs deserve attention. As
organizations defend against APTs, it is crucial to maintain ethical standards and comply with
applicable laws and regulations. Striking a balance between proactive cybersecurity measures
and privacy rights is paramount, ensuring that investigations and mitigation efforts adhere to
legal frameworks.
Additionally, the paper recognizes the importance of continuous monitoring and threat hunting in
the context of APTs. Traditional security measures are often focused on prevention, but APTs
can bypass these defenses and remain undetected for extended periods. Implementing proactive
monitoring solutions, such as Security Information and Event Management (SIEM) systems,
intrusion detection systems (IDS), and endpoint detection and response (EDR) tools, can enable
organizations to detect and respond to APTs in real-time. By actively hunting for indicators of
compromise and anomalous behavior, security teams can identify APT activity early on and
mitigate potential damage.
The paper also emphasizes the significance of ongoing security awareness and training programs
for employees. Human error and negligence can inadvertently open doors for APTs. Educating
employees about the risks associated with APTs, the importance of following security best
practices, and the potential consequences of their actions can significantly reduce the likelihood
of successful APT attacks. Regular training sessions, simulated phishing exercises, and clear
security policies can help establish a security-conscious culture within organizations.
Furthermore, the paper briefly touches on the importance of international cooperation in
combating APTs. APTs often originate from nation-state actors or sophisticated cybercriminal
organizations that operate across borders. Collaboration between governments, cybersecurity
agencies, and international organizations is crucial to share intelligence, coordinate response
efforts, and establish norms and guidelines for responsible behavior in cyberspace. Multilateral
cooperation and information sharing platforms can facilitate swift and effective responses to
APT incidents on a global scale.
Lastly, the paper acknowledges the need for continuous research and innovation to keep pace
with evolving APT techniques. Threat actors constantly adapt their tactics and exploit new
vulnerabilities. Therefore, ongoing research efforts are necessary to identify emerging APT
trends, develop new detection and mitigation techniques, and enhance cybersecurity practices.
Engaging in industry collaborations, participating in cybersecurity conferences and forums, and
supporting academic research can foster innovation and the exchange of knowledge in the field
of APT defense. This includes conducting a comprehensive forensic investigation to determine
the extent of the breach, identifying the vulnerabilities and weaknesses that allowed the APT to
infiltrate the system, and implementing remediation measures to address those gaps. Patching
vulnerabilities, strengthening security controls, and updating security policies and procedures are
essential steps to enhance the organization's resilience against future APT attacks.
APTs often target multiple entities within an industry or sector. By sharing threat intelligence,
organizations can collectively identify patterns, indicators of compromise, and attack techniques
used by APT groups. Collaborative efforts, such as information sharing and analysis centers
(ISACs) and sector-specific cybersecurity partnerships, enable organizations to exchange
actionable intelligence and develop more effective defense strategies against APTs. APT
techniques evolve rapidly, and threat actors constantly innovate to bypass security controls.
Organizations should stay updated on emerging APT trends, new attack vectors, and threat actor
behaviors. This can be achieved through participation in threat intelligence communities,
monitoring cybersecurity news and reports, and engaging with cybersecurity vendors and
experts. By maintaining situational awareness, organizations can adapt their defenses to the
evolving APT landscape.
Building robust security measures into the software development lifecycle, such as secure coding
practices, code reviews, and vulnerability assessments, can significantly reduce the likelihood of
APTs exploiting software vulnerabilities. Implementing secure development frameworks,
performing rigorous testing, and conducting regular code audits contribute to developing more
secure software systems that are resilient against APT attacks. Organizations should regularly
assess the effectiveness of their APT detection and mitigation measures, conduct penetration
testing exercises, and learn from past incidents. By identifying areas of improvement and
implementing lessons learned, organizations can enhance their overall security posture and better
defend against APTs.
Conclusion:
The conclusion section summarizes the key findings of the research and emphasizes the
significance of combating APTs in today's threat landscape. It highlights the importance of
continuous monitoring, proactive detection, and effective mitigation measures to enhance
organizations' resilience against APTs. By implementing comprehensive security measures,
leveraging threat intelligence, and fostering collaboration among stakeholders, organizations can
effectively defend against APTs and safeguard their critical assets.
In conclusion, this research paper emphasizes the need for comprehensive analysis, proactive
detection, and effective mitigation strategies to combat APTs. By understanding the evolving
nature of APTs, leveraging threat intelligence, implementing secure network design, addressing
insider threats, and preparing robust incident response plans, organizations can enhance their
resilience against APTs. Continued research, collaboration, and awareness are crucial to stay
ahead of the evolving APT landscape and ensure the protection of critical assets and sensitive
information.
In conclusion, this research paper highlights the significance of comprehensive strategies to
detect, mitigate, and respond to advanced persistent threats (APTs). By leveraging continuous
monitoring, threat hunting, employee training, international cooperation, and research
advancements, organizations can enhance their defenses against APTs and minimize the
potential impact of these sophisticated cyber threats. A holistic approach that combines technical
measures, human awareness, collaboration, and ongoing research is essential to effectively
combat APTs in an ever-evolving cybersecurity landscape.
By adopting a proactive and collaborative approach, organizations can strengthen their resilience
against APTs and effectively protect their sensitive data and critical assets. Continued research,
industry collaboration, and the integration of best practices are crucial for staying ahead of APT
threats in the ever-changing cybersecurity landscape. By leveraging advanced technologies,
adopting proactive security measures, and fostering collaboration, organizations can enhance
their ability to detect, mitigate, and respond to APTs. Continued research, innovation, and the
integration of best practices are crucial to stay ahead of the evolving APT landscape and
effectively protect critical assets from sophisticated cyber threats.
References
[1] K. Rathor, K. Patil, M. S. Sai Tarun, S. Nikam, D. Patel and S. Ranjit, "A Novel and
Efficient Method to Detect the Face Coverings to Ensurethe Safety using Comparison
Analysis," 2022 International Conference on Edge Computing and Applications (ICECAA),
Tamilnadu, India, 2022, pp. 1664-1667, doi: 10.1109/ICECAA55415.2022.9936392.
[2] Kumar, K. Rathor, S. Vaddi, D. Patel, P. Vanjarapu and M. Maddi, "ECG Based Early Heart
Attack Prediction Using Neural Networks," 2022 3rd International Conference on
Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2022, pp.
1080-1083, doi: 10.1109/ICESC54411.2022.9885448.
[3] K. Rathor, S. Lenka, K. A. Pandya, B. S. Gokulakrishna, S. S. Ananthan and Z. T. Khan, "A
Detailed View on industrial Safety and Health Analytics using Machine Learning Hybrid
Ensemble Techniques," 2022 International Conference on Edge Computing and Applications
(ICECAA), Tamilnadu, India, 2022, pp. 1166-1169, doi:
10.1109/ICECAA55415.2022.9936474.
[4] Manjunath C R, Ketan Rathor, Nandini Kulkarni, Prashant Pandurang Patil, Manoj S. Patil,
& Jasdeep Singh. (2022). Cloud Based DDOS Attack Detection Using Machine Learning
Architectures: Understanding the Potential for Scientific Applications. International Journal
of Intelligent Systems and Applications in Engineering, 10(2s), 268 . Retrieved from
https://www.ijisae.org/index.php/IJISAE/article/view/2398
[5] Wu, Y. (2023). Integrating Generative AI in Education: How ChatGPT Brings Challenges
for Future Learning and Teaching. Journal of Advanced Research in Education, 2(4), 6-10.
[6] K. Rathor, A. Mandawat, K. A. Pandya, B. Teja, F. Khan and Z. T. Khan, "Management of
Shipment Content using Novel Practices of Supply Chain Management and Big Data
Analytics," 2022 International Conference on Augmented Intelligence and Sustainable
Systems (ICAISS), Trichy, India, 2022, pp. 884-887, doi:
10.1109/ICAISS55157.2022.10011003.
[7] S. Rama Krishna, K. Rathor, J. Ranga, A. Soni, S. D and A. K. N, "Artificial Intelligence
Integrated with Big Data Analytics for Enhanced Marketing," 2023 International Conference
on Inventive Computation Technologies (ICICT), Lalitpur, Nepal, 2023, pp. 1073-1077, doi:
10.1109/ICICT57646.2023.10134043.
[8] M. A. Gandhi, V. Karimli Maharram, G. Raja, S. P. Sellapaandi, K. Rathor and K. Singh, "A
Novel Method for Exploring the Store Sales Forecasting using Fuzzy Pruning LS-SVM
Approach," 2023 2nd International Conference on Edge Computing and Applications
(ICECAA), Namakkal, India, 2023, pp. 537-543, doi:
10.1109/ICECAA58104.2023.10212292.
[9] K. Rathor, J. Kaur, U. A. Nayak, S. Kaliappan, R. Maranan and V. Kalpana, "Technological
Evaluation and Software Bug Training using Genetic Algorithm and Time Convolution
Neural Network (GA-TCN)," 2023 Second International Conference on Augmented
Intelligence and Sustainable Systems (ICAISS), Trichy, India, 2023, pp. 7-12, doi:
10.1109/ICAISS58487.2023.10250760.
[10] K. Rathor, S. Vidya, M. Jeeva, M. Karthivel, S. N. Ghate and V. Malathy, "Intelligent
System for ATM Fraud Detection System using C-LSTM Approach," 2023 4th International
Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore,
India, 2023, pp. 1439-1444, doi: 10.1109/ICESC57686.2023.10193398.
[11] K. Rathor, S. Chandre, A. Thillaivanan, M. Naga Raju, V. Sikka and K. Singh, "Archimedes
Optimization with Enhanced Deep Learning based Recommendation System for Drug
Supply Chain Management," 2023 2nd International Conference on Smart Technologies and
Systems for Next Generation Computing (ICSTSN), Villupuram, India, 2023, pp. 1-6, doi:
10.1109/ICSTSN57873.2023.10151666.
[12] Ketan Rathor, "Impact of using Artificial Intelligence-Based Chatgpt Technology for
Achieving Sustainable Supply Chain Management Practices in Selected Industries
," International Journal of Computer Trends and Technology, vol. 71, no. 3, pp. 34-40, 2023.
Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I3P106
[13] "Table of Contents," 2023 2nd International Conference on Smart Technologies and Systems
for Next Generation Computing (ICSTSN), Villupuram, India, 2023, pp. i-iii, doi:
10.1109/ICSTSN57873.2023.10151517.
[14] "Table of Contents," 2023 Second International Conference on Augmented Intelligence and
Sustainable Systems (ICAISS), Trichy, India, 2023, pp. i-xix, doi:
10.1109/ICAISS58487.2023.10250541.
... Cybersecurity threats such as Advanced Persistent Threats (APTs), data breaches, and ransomware attacks are prevalent risks associated with the high connectivity of Supply Chain 4.0 networks [14]. APTs, for example, allow attackers to infiltrate supply chain systems over extended periods, gathering intelligence and potentially disrupting operations without immediate detection [15]The increasing use of IoT devices, which often lack robust security features, further heightens these risks by expanding the attack surface and creating entry points for cyber intrusions [16]. ...
Article
Full-text available
The concept of Supply Chain 4.0 represents a transformative phase in supply chain management through advanced digital technologies like IoT, AI, blockchain, and cyber-physical systems. While these innovations deliver operational improvements, the heightened interconnectivity introduces significant cybersecurity challenges, particularly within military logistics, where mission-critical operations and life-safety concerns are paramount. This paper examines these unique cybersecurity requirements, focusing on advanced persistent threats, supply chain poisoning, and data breaches that could compromise sensitive operations. The study proposes a hybrid cybersecurity framework tailored to military logistics, integrating resilience, redundancy, and cross-jurisdictional security measures. Real-world applicability is validated through simulations, offering strategies for securing supply chains while balancing security, efficiency, and flexibility.
Article
Full-text available
The proliferation of digital technologies has necessitated the integration of sustainable cybersecurity practices to safeguard against escalating threats. This research explores the pivotal role of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in fortifying cybersecurity frameworks for the digital age. By leveraging AI-driven threat detection and ML-powered predictive analytics, this study aims to develop a robust and adaptive cybersecurity paradigm capable of mitigating emerging risks and ensuring the integrity of digital ecosystems. The investigation will delve into the optimization of AI/ML algorithms for enhanced cybersecurity performance, the examination of their applications in threat intelligence and incident response, and the analysis of their implications on sustainable digital transformation. A glimpse of the quantitative results reveals compelling insights: AI-based systems showcased an average threat detection accuracy of 92.5% across diverse cyber threat types, with a minimal false positive rate of 3.2%. The implementation of ML algorithms reduced response times to cyber-attacks by 40%, underscoring their pivotal role in prompt threat mitigation. Furthermore, the research elucidates the efficiency of AI in preventing phishing attacks (95%) and prioritizing critical vulnerabilities for patching, resulting in a 30% reduction in high-risk unpatched vulnerabilities Ultimately, this research seeks to contribute to the development of resilient and sustainable cybersecurity practices, empowering organizations to navigate the complexities of the digital landscape with confidence.
Chapter
This chapter explores the essential organizational and cultural prerequisites for successfully integrating Artificial Intelligence (AI) into network security. This research employs a qualitative methodology, including a comprehensive literature review, to analyze internal needs and address ethical considerations such as bias, privacy, and fairness. This study examines the impact of organizational culture on the acceptance and effectiveness of AI-based solutions. It emphasizes the significance of end-user trust in AI-driven security alerts. The findings highlight the necessity of organizational readiness and cultural adaptation for the effective implementation of AI in network security, concluding that a comprehensive approach is essential for maximizing AI's potential in enhancing security measures. This research will benefit cybersecurity professionals, organizational leaders, and policymakers seeking to understand and navigate the complexities of AI integration in network security.
Article
Full-text available
The processes involved in the supply chain are expected to undergo a radical transformation because of digitalization, which makes use of the technical capabilities of applications for advanced technology. The greater effect of digital technology's use has been largely disregarded owing to a dearth of data pertinent to the topic. This is true even though the technology's advantages to operations are clear. This paper analyses how Char GPT and AI may be used together to increase operational performance, promote sustainable development, and earn money from data that has been acquired. The examples utilized in this study are from the supply chain sector. This project's objective was to conduct an experimental investigation of the tuna fish supply chain in the USA to identify essential end-to-end operations, investigate material and data handling methods, and consider the potential use of artificial intelligence and Chat GPT. Artificial intelligence has the potential to assist in making choices that are data-driven for a wide variety of business problems. Nevertheless, suppose there are problems with the flow of data and information across a supply chain. In that case, the value of AI algorithms may be limited since these algorithms depend on input that is accurate, trustworthy, and timely. Chat GPT can ensure the transparency, accountability, and traceability of such flows because of its potential to act as a hub for the administration and transmission of data and information emanating from a number of sources. The combination of artificial intelligence and chatbots has the potential to assist supply chains in moving beyond the limitations of currently available technology. Then, we can promote operational improvements and implement a dynamic decision-making process by leveraging the complementary effects of these digital technologies. This will allow us to reap the triple-helix sustainability benefits of reducing resource overexploitation, combating fraud, eliminating product recalls, and promoting gender and cultural equality. In the end, the ability to save money and generate more income thanks to data-driven decision-making is a significant boon to the process of monetizing data.
Conference Paper
Full-text available
One crucial step in addressing bugs is assigning a proper severity rating to each report. New bugs are being reported at such a high rate that the bug repository has grown significantly in size. The bug triaging procedure has become more subjective as the size of the bug repository has grown. Therefore, the bug triaging procedure requires a classification of the severity of a problem report. Several machine learning approaches to automatically rank bugs by severity have been proposed. These system performance falls short because insufficient feature patterns are extracted for classifier training. The proposed approach consists of preprocessing, normalization and training the model. For training the model the proposed approach uses GA-TCN.
Conference Paper
Full-text available
ATMs are vulnerable to a wide variety of assaults and fraud because of the money and personal information available on it. In response, today's ATMs feature enhanced hardware security systems are capable of identifying specific forms of fraud and manipulation. However, there is no defense in place for future attacks that can't be anticipated during design. It shows how automated teller machines (ATMs) can be secured against theft without the need for extra hardware. The goal is to employ automati c techniques of model generation to learn normal behavior patterns from the status information of the standard de vices that make up an ATM, with a significant divergence from the taught behavior indicating a fraud attempt. Preprocessing, feature selection, and model training are all parts of the proposed method. Cleaning, integrating, and deduplicating data are all parts of data preprocessing. BOA is employed in feature selection and C-LSTM is used for model training. In C-LSTM, a LSTM recurrent neural network is used to obtain the sentence representation after CNN is used to extract a sequence of higher-level phrase representations. C-LSTM can learn the global and temporal sentence semantics in addition to the local aspects of phrases. When compared to LSTM and CNN, the proposed method fares very well.
Conference Paper
Full-text available
Recent technological advances that have already impacted corporate operations include the internet of things, big data analytics, and artificial intelligence, to name just a few. Artificial intelligence (AI) has the most potential to cause a revolution in marketing strategy compared to other forthcoming technologies. In today's corporate sector, artificial intelligence may be beneficial in a variety of contexts. The intellectual and expert consensus is that artificial intelligence will decide the destiny of human civilization. The expansion of information and communication capabilities has transformed the whole world into a massive network of linked nodes. As a result of technology applications, investments in Artificial Intelligence (AI) for huge data insights to deliver business intelligence have surged. Contrary to common opinion, other industries, including healthcare, e-commerce, education, government, and business, also make major use of Artificial Intelligence technology. An increasing number of businesses frequently use AI technology. Professionals across the globe are attempting to determine which artificial intelligence (AI) solutions are most suited for their advertising campaigns. Yet, a detailed review of the study's findings might emphasize the significance of AI and big data in marketing and suggest future research areas in this sector.
Conference Paper
Full-text available
A supply chain is a mechanism designed to transfer goods from suppliers to customers. This system consists of buyers, producers, workers, information, resources, modes of transportation, and more. The control of the flow of raw materials and completed goods inventory from the point of production until they reach the final customer is known as supply chain management. The manufacturer is the initial link in the chain. You must select a supplier that can produce your product in a secure, economical, and timely way, albeit this will vary depending on the kinds of goods you offer. Demand planning enables you to foresee variations in demand and guarantee that orders are placed at the appropriate time to prevent inventory runs and money being locked up in excess inventory. This may be managed with the use of an inventory control system. The transportation of your product(s) or raw materials must be discussed with your manufacturer in advance. If you have many warehouses, you must make sure that the appropriate amount of merchandise gets to each location and that the freight shipments have the necessary paperwork. At a fulfillment center, merchandise must be properly stored when it is received. For precise and speedy recovery, each SKU requires a separate, special storage place. The process of fulfilling orders placed online is the last link in the supply chain. Picking the products from the order, putting them in a box or poly mailer, and sending the package to the consumer are all necessary steps. Fast shipping and fulfillment might provide your company a competitive edge over rivals.The ultimate reason for the authors to write the paper is to manage the shipment content from ecommerce using the supply chain management practices and Tree structures such as decision tree.
Conference Paper
Full-text available
Industrial work environments are hazardous. Manufacturing facilities contain moving parts equipment, hazardous tools, and ergonomic risks. Falls, moving cars, and large materials are frequent occurrences at construction sites. Forklift traffic, lifting concerns, and even slip and fall dangers are common in warehouses. Even if accidents do occur, there are still things you can do to prevent them. In order to prevent illness and injury in the workplace, employees’ training is essential. According to research, most workplace changes and improvements require practical, small-group training for the safety of the workers that are working in that industry.In all industries, industrial safety and health should be given top priority by all firms. Accidents have typically been attributed to dangerous conduct, hazardous physical working circumstances, or malfunctioning technical systems. Industrial safety is a branch of safety science that attempts to provide businesses with a risk-free, hygienic workplace.The main aim of the paper is to predict if the industry measures are safer for the workers or not with the help of hybrid ensemble techniques.
Conference Paper
Intelligent robots, intelligent mobiles, intelligent stores, and so on are just a few of the areas where computer-aided ergonomics is being put to use. Convenience stores (CVS) are adapting to a new era of competition by offering a wider variety of products and services than ever before, such as daily fresh meals, a cafe, ticketing, and a grocery. Therefore, it is becoming increasingly difficult to estimate daily sales of' fresh commodities due to the impact of both internal and external factors. In the long run, a trustworthy sales-forecasting system is going to be critical for enhancing corporate plans and gaining an edge over the competition. In today's internet age, data production has reached unprecedented levels, well beyond what any single human being can comprehend. This has led to the development of a plethora of machine learning methods. In this proposed approach various machine learning methods are explored for predicting store's sales and evaluate them to find the one that works best for the specific scenario. Training times are reduced and data quality is enhanced with the help of Normalization in the proposed approach. KMeans is a popular feature selection clustering algorithm. Fuzzy Pruning LS-SVM is used in the suggested method for training the model. The proposed model has superior performance on SVM and CNN.
Article
Cloud computing technology has become a crucial component of IT services utilized in daily living in this era of technology. Website hosting services are gradually migrating to cloud in this regard. This increases the value of cloud-based websites while also creating new risks for those services. A severe threat of this nature is DDoS attack. This research propose novel technique in cloud based DDOS attacks using machine learning architectures. here the input has been collected based on cloud module and it has been processed for dimensionality reduction and noise removal. Then this data feature has been extracted and classified using ResNet-101 based KELM. The experimental analysis has been carried out in terms of data delivery ratio, transmission rate, validation accuracy, training accuracy, end-end delay. the proposed technique attained data delivery ratio of 92%, transaction rate of 82%, validation accuracy of 89%, training accuracy of 96%, end-end delay of 56%