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Applied Machine Learning for Analyzing and Defending against Side Channel Threats


The sharing of hardware components in modern processors helps to achieve high performance and meet the increasing computation demand. Though isolation has been done among users and applications at operating system level, recent research shows that attacks can leverage sophisticated approaches to observe the behaviors of the shared hardware components and infer secrets including password, secret key, etc. Such observations and corresponding attacks are called as side channels and side-channel attacks (SCAs). A number of SCAs have been discovered including Flush+Reload, Flush+Flush, Prime+Probe, Spectre, Meltdown, Fallout, RIDL, ZombieLoad. SCAs have threatened the security of billions of hardware devices, including chips manufactured by Intel, Apple, ARM, etc. Therefore, it is urgent to address the security threats caused by SCAs.

This dissertation pursues the use of machine learning to design effective defense mechanisms and obtain a comprehensive understanding of the side channel threats for emerging applications. In particular, we propose to tackle from three aspects: detection, mitigation and vulnerability analysis.

For detection part, we leverage the microarchitecture level information, i.e. hardware performance counters, to build machine learning-based SCAs detectors. Eventually, we propose two customized machine learning classification models to capture SCAs at real-time and detect zero-day SCAs respectively. As the increase edge devices deployed in the network, we also investigate the machine learning-based detectors against malware and SCAs on autonomous vehicles, mobiles and laptops respectively. We find that hardware performance counters can effectively capture the SCAs with machine learning techniques.

A second aspect of the dissertation is exploring the existing system level and hardware level settings for designing light-weight SCAs mitigation approaches. We find that randomizing the frequency and prefetchers can obfuscate side channel traces and protect against secret leakage. Based on the effectiveness of machine learning-based SCAs detection and randomization-based mitigation, we further developed a detection-mitigation defense approach to further minimize performance overhead incurred by adjusting hardware and system level parameters.

In the last part of this dissertation, we evaluated the side channel leakage in more general applications which are mostly neglected in the prior side channel research community. We find that hardware performance counters can also be used by attackers to fingerprint websites users visited. Besides, we also discover that the inputs' labels of deep learning models are susceptible to be leaked via side-channel attack, i.e. Flush+Reload. To the best of our knowledge, we are the first group to identify the correlation between label information and side channel observations, highlighting the importance of reexamining the side channel vulnerability in general applications.

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