EAGER: Detecting and Avoiding Side-Channel Attacks with Security Conscious Prediction
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For many years, computers have been using a technique called "speculation" to achieve good performance. Computers also implement security policies that prevent private data from being revealed to unauthorized entities. Recently, researchers have learned that speculation can unintentionally allow private information to be leaked. An attacker can manipulate speculation to communicate data through a "side-channel," defeating security policies. This project will explore ways to use machine learning to detect whether a computer is under a side-channel attack and trigger defenses that will keep private data from being leaked. The work will enable secure computing while maintaining the benefits of speculation.A predictor will be trained to detect whether the system is under attack, providing a level of confidence in the prediction. Input to the predictor will be features such as counts of microarchitectural events. The predictor will be trained offline and implemented in hardware to be used during execution. Using measurements from real and simulated systems, features correlated with malicious behavior will be explored. Predictors based on neural learning will be trained with those features. The predictor will be prototyped and evaluated in a microarchitectural and circuit simulator. Mitigations based on the predictor confidence will also be prototyped.Side-channel attacks threaten the continued use of speculation to provide high performance. It is expected that this work will enable the continued use of speculation with high confidence in the security of private user data while continuing the much needed level of performance demanded by today''s mobile, server, and embedded applications. Students from under-represented groups will be encouraged to participate in the research. The research will be featured in classroom teaching at Texas A&M University.The project code and data will be made available for at least two years following the completion of the project. The products of this project including technical papers, code archive, and datasets will be made available at http://taco.cse.tamu.edu/secure/.This award reflects NSF''s statutory mission and has been deemed worthy of support through evaluation using the Foundation''s intellectual merit and broader impacts review criteria.