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To improve processor performance, computer architects have adopted such acceleration techniques as speculative execution and caching. However, researchers have recently discovered that this approach implies inherent security flaws, as exploited by Meltdown and Spectre. Attacks targeting these vulnerabilities can leak protected data through side channels such as data cache timing by exploiting mis-speculated executions. The flaws can be catastrophic because they are fundamental and widespread and they affect many modern processors. Mitigating the effect of Meltdown is relatively straightforward in that it entails a software-based fix which has already been deployed by major OS vendors. However, to this day, there is no effective mitigation to Spectre. Fixing the problem may require a redesign of the architecture for conditional execution in future processors. In addition, a Spectre attack is hard to detect using traditional software-based antivirus techniques because it does not leave traces in traditional log files. In this paper, we proposed to monitor microarchitectural events such as cache misses, branch mispredictions from existing CPU performance counters to detect Spectre during attack runtime. Our detector was able to achieve 0% false negatives with less than 1% false positives using various machine learning classifiers with a reasonable performance overhead.
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Automated Detection of Spectre and Meltdown Attacks Using Explainable Machine Learning
Spectre and Meltdown attacks exploit security vulnerabilities of advanced architectural features to access inherently concealed memory data without authorization. Existing defense mechanisms have three major drawbacks: (i) they can be fooled by obfuscation techniques, (ii) the lack of transparency severely limits their applicability, and (iii) it can introduce unacceptable performance degradation. In this paper, we propose a novel detection scheme based on explainable machine learning to address these fundamental challenges. Specifically, this paper makes three important contributions. (1) Our work is the first attempt in applying explainable machine learning for Spectre and Meltdown attack detection. (2) Our proposed method utilizes the temporal differences of hardware events in sequential timestamps instead of overall statistics, which contributes to the robustness of ML models against evasive attacks. (3) Extensive experimental evaluation demonstrates that our approach can significantly improve detection efficiency (38.4% on average) compared to state-of-the-art techniques.
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- Award ID(s):
- 1908131
- PAR ID:
- 10354095
- Date Published:
- Journal Name:
- IEEE International Symposium on Hardware Oriented Security and Trust (HOST)
- Page Range / eLocation ID:
- 24 to 34
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/HOST49136.2021.9702278
