Machine Learning-Based Vulnerability Assessment for the IT Infrastructure of Industrial Companies

Osama Hosam (), Rasha Abousamra () and Osama Dandash ()
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Osama Hosam: Higher Colleges of Technology
Rasha Abousamra: Higher Colleges of Technology
Osama Dandash: Higher Colleges of Technology

Chapter Chapter 9 in The Palgrave Handbook of Breakthrough Technologies in Contemporary Organisations, 2025, pp 107-120 from Springer

Abstract: Abstract Regular and frequent vulnerability and risk assessment operations are critical for understanding the possible risks to the information security infrastructure within an organisation. Vulnerability assessment is important for a security analyst to understand the potential problems and vulnerabilities in the information technology environment. Threats and weaknesses in a security system can lead to significant risks to the organisation’s data and service continuity. In this chapter, a framework will be introduced to analyse the current security status of the organisational security infrastructure. The framework contains four phases: Threat and Vulnerability Identification, Threat and Vulnerability Analysis, Risk Evaluation, and Mitigation Plan. Machine learning capabilities are incorporated in each phase to enhance the efficiency and effectiveness of the vulnerability assessment process. For example, machine learning algorithms are used for vulnerability scanning to automatically learn from historical data and identify patterns. They are also used for vulnerability analysis, risk assessment, and remediation by automatically analysing and prioritising vulnerabilities based on their potential impact and likelihood of being exploited. Additionally, they suggest the most effective strategies for mitigating or eliminating vulnerabilities. The framework is found to be rigorous and competitive. It increases the ability to capture the current security posture of an enterprise and presents a comprehensive approach for the analysis and monitoring of enterprise networks.

Keywords: Vulnerabilities; Vulnerability assessment; Risk assessment; Information security; Machine learning; Mitigation plan (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-981-96-2516-1_9

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