engleski

A Systematic Evaluation of Profiling through Focused Feature Selection

Profiled side-channel attacks consist of several steps one needs to take. An important, but sometimes ignored, step is a selection of the points of interest (features) within side- channel measurement traces. A large majority of the related works start the analyses with an assumption that the features are preselected. Contrary to this assumption, here we concentrate on the feature selection step. We investigate how advanced feature selection techniques stemming from the machine learning domain can be used to improve the attack efficiency. To this end, we provide a systematic evaluation of the methods of interest. The experiments are performed on several real-world datasets containing software and hardware implementations of AES, including the random delay countermeasure. Our results show that Wrapper and Hybrid feature selection methods perform extremely well over a wide range of test scenarios and a number of features selected. We emphasize L1 regularization (Wrapper approach) and Linear SVM with recursive feature elimination used after chi square filter (Hybrid approach) that perform well in both accuracy and guessing entropy. Finally, we show that the use of appropriate feature selection techniques is more important for an attack on the high-noise datasets, including those with countermeasures than on the low-noise ones.

profiled side-channel attacks, feature selection, machine learning, guessing entropy, random delay countermeasure

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