Paper 2019/168

Profiling Side-channel Analysis in the Efficient Attacker Framework

Stjepan Picek, Annelie Heuser, Guilherme Perin, and Sylvain Guilley


Profiling side-channel attacks represent the most powerful category of side-channel attacks. There, we assume that the attacker has access to a clone device to profile its leaking behavior. Additionally, we consider the attacker to be unbounded in power to give the worst-case security analysis. In this paper, we start with a different premise where we are interested in the minimum strength that the attacker requires to conduct a successful attack. To that end, we propose a new framework for profiling side-channel analysis that we call the Efficient Attacker Framework. With it, we require the attackers to use as powerful attacks as possible, but we also provide a setting that inherently allows a more objective analysis among attacks. We discuss the ramifications of having the attacker with unlimited power when considering the neural network-based attacks. There, we show that the Universal Approximation Theorem can be connected with neural network-based attacks able to break implementations with only a single measurement. Those considerations further strengthen the need for the Efficient Attacker Framework. To confirm our theoretical results, we provide an experimental evaluation of our framework.

Available format(s)
Publication info
Side-channel analysisMachine learningDeep learningEfficient Attacker Framework
Contact author(s)
picek stjepan @ gmail com
annelie heuser @ irisa fr
2020-05-30: revised
2019-02-20: received
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Creative Commons Attribution


      author = {Stjepan Picek and Annelie Heuser and Guilherme Perin and Sylvain Guilley},
      title = {Profiling Side-channel Analysis in the Efficient Attacker Framework},
      howpublished = {Cryptology ePrint Archive, Paper 2019/168},
      year = {2019},
      note = {\url{}},
      url = {}
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