The biasvariance decomposition in profiled attacks
The profiled attacks challenge the security of cryptographic devices in the worst case scenario. We elucidate the reasons underlying the success of different profiled attacks (that depend essentially on the context) based on the well-known biasvariance tradeoff developed in the machine learning field. Note that our approach can easily be extended to non-profiled attacks. We show (1) how to decompose (in three additive components) the error rate of an attack based on the biasvariance decomposition, and (2) how to reduce the error rate of a model based on the biasvariance diagnostic. Intuitively, we show that different models having the same error rate require different strategies (according to the biasvariance decomposition) to reduce their errors. More precisely, the success rate of a strategy depends on several criteria such as its complexity, the leakage information and the number of points per trace. As a result, a suboptimal strategy in a specific context can lead the adversary to overestimate the security level of the cryptographic device. Our results also bring warnings related to the estimation of the success rate of a profiled attack that can lead the evaluator to underestimate the security level. In brief, certify that a chip leaks (or not) sensitive information represents a hard if not impossible task.
biasvariance decomposition profiled attack machine learning side-channel attack stochastic attack template attack