Machine learning algorithms fall prey to adversarial examples. As profiling side-channel attacks are seeing rapid adoption of machine learning-based approaches that can even defeat commonly used side-channel countermeasures, we investigate the potential of adversarial example as a defense mechanism. We show that adversarial examples have the potential to serve as a countermeasure against machine learning-based side-channel attacks. Further, we exploit the transferability property to show that a common adversarial example can act as a countermeasure against a range of machine learning-based side-channel classifiers.

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The interplay between machine learning and security is becoming more prominent. New applications using machine learning also bring new security risks. Here, we show it is possible to reverse-engineer the inputs to a neural network with only a single-shot side-channel measurement assuming the attacker knows the neural network architecture being used.

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Side-channel attacks are a real threat to many secure systems. In this paper, we consider two ciphers used in the automotive industry – AES and ChaCha20 and we evaluate their resistance against side-channel attacks. In particular, the focus is laid upon the main non-linear component in these ciphers. Owing to the design of ChaCha20, it offers natural timing side-channel resistance and thus is suitable for affected applications. However, attacks exploiting the power side-channel are somewhat more difficult on ChaCha20 as compared to AES, but the overhead to protect ChaCha20 against such attack is considerably higher.@en