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MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection.
Jiaxun
Cui, Xiaomeng Yang, Mulong Luo, Geunbae
Lee, Peter Stone, Hsien-Hsin
S. Lee, Benjamin Lee, G. Edward Suh, Wenjie Xiong, and
Yuandong Tian.
In The Eleventh International Conference on Learning
Representations (ICLR), May 2023.
Video presentation
[PDF]769.8kB [slides.pdf]2.1MB [poster.pdf]1.8MB
Security vulnerabilities in computer systems raise serious concerns as computers process an unprecedented amount of private and sensitive data today. Cache timing attacks (CTA) pose an important practical threat as they can effectively breach many protection mechanisms in today’s systems. However, the current detection techniques for cache timing attacks heavily rely on heuristics and expert knowledge, which can lead to brittleness and the inability to adapt to new attacks. To mitigate the CTA threat, we propose MACTA, a multi-agent reinforcement learning (MARL) approach that leverages population-based training to train both attackers and detectors. Following best practices, we develop a realistic simulated MARL environment, MA-AUTOCAT, which enables training and evaluation of cache-timing attackers and detectors. Our empirical results suggest that MACTA is an effective solution without any manual input from security experts. MACTA detectors can generalize to a heuristic attack not exposed in training with a 97.8 percent detection rate and reduce the attack bandwidth of adaptive attackers by 20 percent on average. In the meantime, MACTA attackers are qualitatively more effective than other attacks studied, and the average evasion rate of MACTA attackers against an unseen state-of-the-art detector can reach up to 99 percent. Furthermore, we found that agents equipped with a Transformer encoder can learn effective policies in situations when agents with multi-layer perceptron encoders do not in this environment, suggesting the potential of Transformer structures in CTA problems.
@InProceedings{ICLR23-Cui,
author = {Jiaxun Cui and Xiaomeng Yang and Mulong Luo and Geunbae Lee and Peter Stone and Hsien-Hsin S. Lee and Benjamin Lee and G. Edward Suh and Wenjie Xiong and Yuandong Tian},
title = {MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection},
booktitle = {The Eleventh International Conference on Learning Representations (ICLR)},
location = {Kigali, Rwanda},
month = {May},
year = {2023},
abstract = {
Security vulnerabilities in computer systems raise serious concerns
as computers process an unprecedented amount of private and sensitive
data today. Cache timing attacks (CTA) pose an important practical
threat as they can effectively breach many protection mechanisms in
today’s systems. However, the current detection techniques for cache
timing attacks heavily rely on heuristics and expert knowledge, which
can lead to brittleness and the inability to adapt to new attacks.
To mitigate the CTA threat, we propose MACTA, a multi-agent reinforcement
learning (MARL) approach that leverages population-based training to
train both attackers and detectors. Following best practices, we develop
a realistic simulated MARL environment, MA-AUTOCAT, which enables training
and evaluation of cache-timing attackers and detectors. Our empirical results
suggest that MACTA is an effective solution without any manual input from
security experts. MACTA detectors can generalize to a heuristic attack not
exposed in training with a 97.8 percent detection rate and reduce the attack
bandwidth of adaptive attackers by 20 percent on average. In the meantime,
MACTA attackers are qualitatively more effective than other attacks studied,
and the average evasion rate of MACTA attackers against an unseen
state-of-the-art detector can reach up to 99 percent. Furthermore, we found
that agents equipped with a Transformer encoder can learn effective policies
in situations when agents with multi-layer perceptron encoders do not in this
environment, suggesting the potential of Transformer structures in CTA problems.
},
wwwnote={<a href="https://iclr.cc/virtual/2023/poster/11111">Video presentation</a>},
}
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