Adaptive Channel Estimation in Side Channel Attacks
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abstract
2018 IEEE. Side channel attacks exploit physical information that leaks from the target implementations to extract secret information, for example keys. Since the observable physical leakages are dependent on the internal state of the cryptographic implementation, predicting (or profiling) this dependency, which is also called leakage model or leakage function, can significantly improve the performance of attacks. Previous work shows that the leakage function (side channel) can be represented as a noisy channel in communication theory. In this paper, we focus on how to model the side channel as a fading channel. We treat the leakage model coefficients as channel fading (gains), to develop a weighted leakage model. Under this assumption, the profiling problem in side channel attacks becomes a channel estimation problem in communication. This paper also proposes al2-norm based reweighted algorithm for estimating the leakage model. Compared to previous methods, such as the Least Squares and the Ridge-Based method, our algorithm shows significant improvements both in terms of performance of recovery and efficiency of implementation.
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2018 IEEE International Workshop on Information Forensics and Security (WIFS)
