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Erschienen in:
2013 | OriginalPaper | Buchkapitel
An Unsupervised Learning Model to Perform Side Channel Attack
verfasst von : Jung-Wei Chou, Min-Huang Chu, Yi-Lin Tsai, Yun Jin, Chen-Mou Cheng, Shou-De Lin
Erschienen in: Advances in Knowledge Discovery and Data Mining
Verlag: Springer Berlin Heidelberg
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This paper proposes a novel unsupervised learning approach for Power Analysis – a form of side channel attack in Cryptanalysis. Different from existing works that exploit supervised learning framework to solve this problem, our method does not require any labeled pairs, which contains information of the form {X,Y}={key, power-trace}, but is still capable of deciphering the secret key accurately. Besides proposing a regression-based, unsupervised approach for this purpose, we further propose an enhanced model through exploiting the dependency of key bits between different sub-processes during the encryption process to obtain accurate results in a more efficient way. Our experiment shows that the proposed method outperforms the state-of-the-art non-learning based decipherment methods significantly.