Enhancing Prime+Probe Attack Detection Using AI and Machine Learning Techniques for Improved Security

This research paper explores the complex area of PRIME+PROBE attacks, an advanced kind of cache-based side-channel attack that presents serious security risks. The study includes a thorough analysis of PRIME+PROBE attacks, including a thorough description of their key methods. It also compares various hardware performance counter measurement techniques, providing information on how well they can catch these evasive attacks. An experimental setup for replicating PRIME+PROBE attacks on Intel CPUs is described in order to experimentally evaluate the findings. This configuration offers the framework for obtaining important information and insights. The paper then uses three machine learning techniques to identify PRIME+PROBE attacks utilizing hardware performance counter features: Support Vector Machine (SVM), Neural Network, and Gradient Boosting. The findings of this study provide important light on the capabilities and constraints of machine learning algorithms for countering PRIME+PROBE attacks as well as hardware performance counter measurement instruments. The research makes a substantial contribution to the field of cybersecurity and lays the groundwork for the creation of effective defenses against these elusive and possibly catastrophic threats.