KSM: Killer of Spectre and Meltdown Attacks
Zhongkai Tong, Ziyuan Zhu, Yusha Zhang, Yuxin Liu, Dan Meng · 2024
In the relentless pursuit of bolstering processor performance, computer architects have harnessed a gamut of sophisticated optimization techniques. However, this pursuit of performance enhancements has inadvertently laid bare an underbelly of concealed security vulnerabilities, exemplified by notorious instances such as Meltdown and Spectre attacks. These attacks adeptly exploit optimization techniques, coupled with cache side-channel attacks, to expose protected data. The ripple effects of these vulnerabilities are indeed seismic, owing to their pervasive presence across existing and forthcoming processors. Regrettably, Meltdown and Spectre have remained elusive to satisfactory mitigation to date; instead, Spectre and Meltdown attack variations have sprung forth from them. In response to this challenge, this paper posits an approach. It proposes the optimization of four distinctive hardware performance events through feature selection, subsequently harnessing the prowess of machine learning algorithms to forge a real-time detection mechanism. This mechanism is primed to combat Spectre V1, V2, V4, and Meltdown attack variations, culminating in a robust accuracy rate exceeding 99%. This resounding success demonstrates that this paper’s framework not only confronts original attacks but also grapples effectively with diverse attack variants, a scenario that might manifest in everyday contexts.