PerSpectron：利用感知器检测微架构攻击的不变足迹
PerSpectron: Detecting Invariant Footprints of Microarchitectural Attacks with Perceptron
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中文总结 AI 辅助
本研究提出PerSpectron，一种基于感知器学习的硬件检测器，通过监控微架构统计特征，在数据泄露前检测并分类Spectre、Meltdown等微架构攻击，具有低开销且不增加计算延迟。
中文摘要 AI 辅助
鉴于微架构攻击近年来的泛滥，检测这类攻击至关重要。许多此类攻击表现出对其操作本质至关重要的内在行为，例如制造资源争用或错误推测。本研究系统性地调查了基于硬件的攻击的微架构足迹，并展示了如何利用一种高效的硬件预测器来检测和分类这些攻击。我们提出了一种方法，利用相关的微架构统计数据来设计一个基于硬件的神经预测器，能够在数据泄露之前检测和分类微架构攻击。一旦检测到潜在攻击，可以通过触发适当的对策来主动缓解。我们的基于硬件的检测器PerSpectron使用感知器学习来识别和分类攻击。基于感知器的预测已成功用于分支预测和其他基于硬件的应用。PerSpectron的性能开销极小。所监控的统计数据的开销与现有的性能监控计数器类似。此外，PerSpectron在处理器关键路径之外运行，提供安全性而不会增加计算延迟。我们的系统在检测SpectreV1、SpectreV2、SpectreRSB、Meltdown、breakingKSLR、Flush+Flush、Flush+Reload、Prime+Probe等攻击以及缓存攻击校准程序方面达到了可用的检测率。我们还相信，大量多样的微架构特征提供了规避弹性和可解释性——这些是以前的硬件安全检测器所不具备的特性。与之前仅在数据暴露后才触发对策的工作不同，我们足够早地检测到这些攻击，以避免任何数据泄露。
英文摘要
Detecting microarchitectural attacks is critical given their proliferation in recent years. Many of these attacks exhibit intrinsic behaviors essential to the nature of their operation, such as creating contention or misspeculation. This study systematically investigates the microarchitectural footprints of hardware-based attacks and shows how they can be detected and classified using an efficient hardware predictor. We present a methodology to use correlated microarchitectural statistics to design a hardware-based neural predictor capable of detecting and classifying microarchitectural attacks before data is leaked. Once a potential attack is detected, it can be proactively mitigated by triggering appropriate countermeasures. Our hardware-based detector, PerSpectron, uses perceptron learning to identify and classify attacks. Perceptron-based prediction has been successfully used in branch prediction and other hardware-based applications. PerSpectron has minimal performance overhead. The statistics being monitored have similar overhead to already existing performance monitoring counters. Additionally, PerSpectron operates outside the processor's critical paths, offering security without added computation delay. Our system achieves a usable detection rate for detecting attacks such as SpectreV1, SpectreV2, SpectreRSB, Meltdown, breakingKSLR, Flush+Flush, Flush+Reload, Prime+Probe as well as cache-attack calibration programs. We also believe that the large number of diverse microarchitectural features offers both evasion resilience and interpretability---features not present in previous hardware security detectors. We detect these attacks early enough to avoid any data leakage, unlike previous work that triggers countermeasures only after data has been exposed.
发表机构
- Texas A&M University（德克萨斯农工大学）
- Intel Labs（英特尔实验室）
- University of California, Riverside（加州大学河滨分校）
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