Core‐level cybersecurity assurance using cloud‐based adaptive machine learning techniques for manufacturing industry

Sakthivel, Rakesh Kumar; Nagasubramanian, Gayathri; Al‐Turjman, Fadi; Sankayya, Muthuramalingam

doi:10.1002/ett.3947

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…As such, it is crucial to define and develop some confidence metrics for security assurance level and the selection and aggregation technique for these security metrics. As pointed out in [250], operating system core can be considered instead of the application level service in order to increase the speed and effectiveness of attack detection [251]- [256]. This is because the operating system's core contains every internal attribute and the file system.…”

Section: Research Gaps and Recommendationsmentioning

confidence: 99%

Software security models and frameworks: an overview and current trends

Korir¹

2023

World J. Adv. Eng. Technol. Sci.

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The continued dependence on information technology applications has led to the adoption of electronic channels and software applications to support businesses, online transactions and communications. In this perspective, both functional and non-functional requirements are critical for the provision of necessary needs at the early phases of the software development process. This is specifically important in the requirement phase of the software development process. Software security is increasingly becoming a necessity concern in this environment. Unfortunately, security concepts such as access control requirements are mostly considered after the functional requirement definition stage. In addition, majority of the developers and organizations regard security as an activity that can be incorporated after the development of a system. This leads to flaws and security defects in the access control mechanism. Therefore, software security issues must be given higher priority in the early stages of the development process. The aim of this paper is to offer a survey of the software security techniques, frameworks and models that have been developed to deal with these issues. The results obtained indicate that software vulnerabilities have made it possible for attackers to exploit them to cause havoc in computerized systems and has become one of the most critical risks facing modern corporate networks. Since most of these vulnerabilities involve exploitable weaknesses introduced through badly written code, the cyber security community has tried to come up with techniques to enhance software products.

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Section: Research Gaps and Recommendationsmentioning

confidence: 99%

Software security models and frameworks: an overview and current trends

Korir¹

2023

World J. Adv. Eng. Technol. Sci.

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“…The dataset used was from the Cardiff University Research Portal. In another research work [80], anomalous services running into the computer systems, both offline and online, were iden-tified using a neural network-based model (NARX-RNN), AI-based multi-perspective SVM, principal component analysis (PCA) and hierarchical process tree-based reinforcement learning techniques.…”

Section: Stages Of a Cyber-attackmentioning

confidence: 99%

Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review

Ahsan

Nygard

Gomes

et al. 2022

JCP

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Machine learning is of rising importance in cybersecurity. The primary objective of applying machine learning in cybersecurity is to make the process of malware detection more actionable, scalable and effective than traditional approaches, which require human intervention. The cybersecurity domain involves machine learning challenges that require efficient methodical and theoretical handling. Several machine learning and statistical methods, such as deep learning, support vector machines and Bayesian classification, among others, have proven effective in mitigating cyber-attacks. The detection of hidden trends and insights from network data and building of a corresponding data-driven machine learning model to prevent these attacks is vital to design intelligent security systems. In this survey, the focus is on the machine learning techniques that have been implemented on cybersecurity data to make these systems secure. Existing cybersecurity threats and how machine learning techniques have been used to mitigate these threats have been discussed. The shortcomings of these state-of-the-art models and how attack patterns have evolved over the past decade have also been presented. Our goal is to assess how effective these machine learning techniques are against the ever-increasing threat of malware that plagues our online community.

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“…Li et al [28] combined distributed streaming processing mechanism and ensemble prediction algorithm to monitor the abnormal network traffic of a cyberphysical power system. Sakthivel et al [29] employed a Recursive Neural Network (RNN) algorithm to monitor and prevent the cyber system from cyberattacks in a manufacturing company. Garrido et al [30] adopted a graph learning algorithm to detect intrusion, score anomalous activities and monitor security in industrial automation systems.…”

Section: Machine Learning In Risk Managementmentioning

confidence: 99%

A preliminary SWOT evaluation for the applications of ML to Cyber Risk Analysis in the Construction Industry

Yao

Soto

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Construction 4.0 is driving construction towards a data-centered industry. Construction firms manage significant amounts of valuable digital information, making them the target of cyberattacks, which not only compromise stored information but could cause severe harm to cyber-physical systems, personnel, and products. Therefore, it is critical to conduct cyber risk analyses to manage construction information assets to ensure their confidentiality, integrity, and availability. Traditional risk analysis methodologies like Fault Tree Analysis have limitations in dealing with the rapidly evolving cyber risks. As an alternative, Machine Learning (ML) methods are finding their way into the risk analysis field. ML models developed for cybersecurity purposes can learn from past results to make reliable predictions while removing the laboriousness of the traditional risk analysis. This article reviews ML techniques used for cyber risk analysis in different industries in recent years. Based on that, we investigate how ML techniques could be used for cyber risk analysis. Afterward, a SWOT analysis is conducted to identify the Strengths, Weaknesses, Opportunities, and Threats regarding the applications of ML in cyber risk analysis in the construction industry, and recommendations to address the weaknesses and threats are presented. Finally, future research areas using ML to prevent cyberattacks in the construction industry are proposed.

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Core‐level cybersecurity assurance using cloud‐based adaptive machine learning techniques for manufacturing industry

Cited by 12 publications

References 25 publications

Software security models and frameworks: an overview and current trends

Software security models and frameworks: an overview and current trends

Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review

A preliminary SWOT evaluation for the applications of ML to Cyber Risk Analysis in the Construction Industry

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