Assessing Vulnerabilities in Software and Hardware Systems

Abstract

Understanding vulnerabilities in software and hardware is crucial for effective cybersecurity. A vulnerability allows attackers to breach system confidentiality, integrity, or availability. This research introduces a novel machine learning framework aimed at enhancing vulnerability detection accuracy while reducing false positive rates. We discuss principles of vulnerability assessment methodologies, particularly for products combining software and hardware. The study reviews various exploitation methods, including cyber-physical and side-channel attacks, outlining their locations and potential impacts. We present case studies on software vulnerabilities using multiple detection tools and explore the influence of emerging technologies, such as quantum computing, on detection methods. Our findings emphasize the need for proactive measures in risk management and highlight five security priorities that organizations should adopt. Further research is essential to address unlisted software vulnerabilities and improve detection methodologies.