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To improve the overall performance of processors, computer architects usevarious performance optimization techniques in modern processors, such asspeculative execution, branch prediction, and chaotic execution. Both now andin the future, these optimization techniques are critical for improving theexecution speed of processor instructions. However, researchers have discoveredthat these techniques introduce hidden inherent security flaws, such asmeltdown and ghost attacks in recent years. They exploit techniques such aschaotic execution or speculative execution combined with cache-basedside-channel attacks to leak protected data. The impact of thesevulnerabilities is enormous because they are prevalent in existing or futureprocessors. However, until today, meltdown and ghost have not been effectivelyaddressed, but instead, multiple attack variants and different attackimplementations have evolved from them. This paper proposes to optimize fourdifferent hardware performance events through feature selection and use machinelearning algorithms to build a real-time detection mechanism for Spectrev1,v2,v4, and different implementations of meltdown attacks, ultimatelyachieving an accuracy rate of over 99\%. In order to verify the practicality ofthe attack detection model, this paper is tested with a variety of benignprograms and different implementations of Spectre attacks different from themodeling process, and the absolute accuracy also exceeds 99\%, showing thatthis paper can cope with different attack variants and differentimplementations of the same attack that may occur daily.

Attack detection based on machine learning algorithms for different variants of Spectre attacks and different Meltdown attack implementations