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arXiv 2006.01442cs.CR
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利用机器学习实时的检测Spectre和Meltdown攻击

Real time Detection of Spectre and Meltdown Attacks Using Machine Learning

Bilal Ali Ahmad

摘要:

最近发现的Spectre和meltdown攻击通过侧信道攻击将机密信息泄露给其他进程,从而影响几乎所有处理器。这些漏洞暴露了现代CPU架构中的设计缺陷。为了修复这些设计缺陷,有必要对现代处理器的硬件进行更改,这是一项不平凡的任务。针对这些漏洞的软件缓解技术会导致显著的性能下降。为了在保持现代处理器性能优势的同时减轻Spectre和Meltdown攻击,本文提出了一种通过识别投机执行和侧信道攻击的滥用来实时检测Spectre和Meltdown攻击的机制。我们使用硬件性能计数器和软件事件来监控与推测执行、分支预测和缓存干扰相关的活动。我们使用各种机器学习模型来分析这些事件。当系统受到攻击时,这些事件会产生非常独特的模式;机器学习模型能够在实际负载条件下检测到Meltdown和Spectre攻击,准确率超过99%。

英文摘要:

Recently discovered Spectre and meltdown attacks affects almost all processors by leaking confidential information to other processes through side-channel attacks. These vulnerabilities expose design flaws in the architecture of modern CPUs. To fix these design flaws, it is necessary to make changes in the hardware of modern processors which is a non-trivial task. Software mitigation techniques for these vulnerabilities cause significant performance degradation. In order to mitigate against Spectre and Meltdown attacks while retaining the performance benefits of modern processors, in this paper, we present a real-time detection mechanism for Spectre and Meltdown attacks by identifying the misuse of speculative execution and side-channel attacks. We use hardware performance counters and software events to monitor activity related to speculative execution, branch prediction, and cache interference. We use various machine learning models to analyze these events. These events produce a very distinctive pattern while the system is under attack; machine learning models are able to detect Meltdown and Spectre attacks under realistic load conditions with an accuracy of over 99%.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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