Exploit Detection and Mitigation Technique of Cache Side-Channel Attacks using Artificial Intelligence

As a persistent danger to computer security, cache side-channel attacks take advantage of minute flaws in microarchitectures to access sensitive data without authorization. In-depth analysis of these attacks’ numerous forms and related Common Vulnerabilities and Exposures (CVEs) is provided in this study report. The study emphasizes cache side-channel attacks’ practical ramifications and how they affect system security. The methods for identifying these threats are examined, with an emphasis on how machine learning and deep learning approaches might be used to strengthen security precautions. The research offers useful insights via pseudo code and data structure figures that highlight the necessity of real-time monitoring and detection tactics in reducing cache side-channel vulnerabilities. Artificial intelligence is used to design proactive countermeasures. The research article calls for proactive security measures to thwart these developing risks in addition to illuminating the complicated landscape of cache side-channel attacks. The contribution to this domain goes beyond theoretical discussion and includes actual application. This paper demonstrates the critical relevance of real-time monitoring and detection tactics in the fight to defend systems from cache side-channel attacks via the presentation of pseudo code and data structure diagrams. The results of this work provide guidance for future research and highlight the crucial value of interdisciplinary cooperation in the continuous quest for safe computing systems.