Comprehensive Evaluation of Machine Learning Countermeasures for Detecting Microarchitectural Side-Channel Attacks
Comprehensive Evaluation of Machine Learning Countermeasures for Detecting Microarchitectural Side-Channel Attacks
复制标题
检测微架构侧通道攻击的机器学习对策的综合评估
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
H. Homayoun
中科院分区：
文献类型：
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作者：
Han Wang;H. Sayadi;Avesta Sasan;S. Rafatirad;T. Mohsenin;H. Homayoun
微架构侧信道攻击（SCA）对现代计算系统的安全性构成了严重威胁。此类攻击利用源自诸如高速缓冲存储器等基本性能增强组件的侧信道漏洞。现有的基于低级微架构特征检测SCA的工作考虑收集从处理器的硬件性能计数器（HPC）寄存器获取的受害应用和攻击应用的硬件事件。然而，在这类技术中，攻击的HPC数据可能很容易被操纵和/或篡改，从而误导SCA检测机制。此外，先前的研究探索了有限数量的机器学习（ML）算法在检测微架构SCA方面的适用性。作为回应，在本文中，我们基于低级微架构特征对用于实时侧信道攻击检测的各种基于机器学习的对策进行了全面评估。为此，使用HPC特征收集受害应用的行为，并在无攻击和攻击条件下进行分析，以避免攻击者的HPC被潜在操纵。我们进一步探究了在不同间隔对微架构特征进行采样时HPC的监测开销，以找出用于SCA检测的合适采样间隔。为了进行全面分析，我们实现了各种类型的ML分类器，并在不同的评估指标（包括检测准确性、F值、鲁棒性（ROC曲线下面积）和计算延迟）之间进行了精确比较，以确定用于实时微架构SCA检测的最有效ML分类器。
Microarchitectural Side-Channel Attacks (SCAs) have posed serious threats to the security of modern computing systems. Such attacks exploit side-channel vulnerabilities stemming from fundamental performance-enhancing components such as cache memories. The existing works on detection of SCAs based on low-level microarchitectural features have considered collecting both victim and attack applications' hardware events that are captured from processors' hardware performance counter (HPC) registers. However, in such techniques the attack HPCs data can be easily manipulated and/or corrupted resulting in misleading the SCAs detection mechanism. In addition, the prior studies have explored the suitability of a limited number of Machine Learning (ML) algorithms in detecting microarchitectural SCAs. In response, in this paper, we conduct a comprehensive evaluation of various machine learning-based countermeasures for real-time side-channel attack detection based on low-level microarchitectural features. For this purpose, the victim applications' behavior is collected using the HPC features and analyzed under no attack and attack conditions to avoid potential manipulation of attackers' HPCs. We further explore the HPCs monitoring overhead when microarchitectural features are sampled at different intervals to find out the appropriate sampling interval for SCAs detection. For the purpose of thorough analysis, various types of ML classifiers are implemented and precisely compared across different evaluation metrics including detection accuracy, F-measure, robustness (Area Under the ROC Curve), and computational latency to identify the most efficient ML classifiers for real-time microarchitectural SCAs detection
SCARF：使用低级硬件功能实时检测侧信道攻击
DOI：
10.1109/iolts50870.2020.9159708
发表时间：
2020
期刊：
IEEE International Symposium on On-Line Testing and Robust System Design
影响因子：
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作者：
Wang, Han;Sayadi, Hossein;Rafatirad, Setareh;Sasan, Avesta;Homayoun, Houman
通讯作者：
Homayoun, Houman
使用性能计数器的微架构跟踪在线检测幽灵攻击
DOI：
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发表时间：
2018
期刊：
Proceedings of the 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2018
影响因子：
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作者：
Gaudiot, J-L.
通讯作者：
Gaudiot, J-L.