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Recently discovered Spectre and meltdown attacks affects almost allprocessors by leaking confidential information to other processes throughside-channel attacks. These vulnerabilities expose design flaws in thearchitecture of modern CPUs. To fix these design flaws, it is necessary to makechanges in the hardware of modern processors which is a non-trivial task.Software mitigation techniques for these vulnerabilities cause significantperformance degradation. In order to mitigate against Spectre and Meltdownattacks while retaining the performance benefits of modern processors, in thispaper, we present a real-time detection mechanism for Spectre and Meltdownattacks by identifying the misuse of speculative execution and side-channelattacks. We use hardware performance counters and software events to monitoractivity related to speculative execution, branch prediction, and cacheinterference. We use various machine learning models to analyze these events.These events produce a very distinctive pattern while the system is underattack; machine learning models are able to detect Meltdown and Spectre attacksunder realistic load conditions with an accuracy of over 99%.

Real time Detection of Spectre and Meltdown Attacks Using Machine Learning