Generic-Compatible Distinguishers for Linear Regression Based Attacks

Non profiled attacks aim to recover secret information from a device without prior knowledge of its leakage model. However, most practical non profiled attacks, such as Differential Power Analysis, Correlation Power Analysis, and Linear Regression-based Attacks (LRA), still depend on a priori leakage assumptions. Designing a generic attack that does not rely on any such assumption therefore remains an open problem and has been actively investigated by the side-channel community for more than a decade. Although Whitnall et al. showed that LRA can be considered generic when all predictors are included, this is not feasible in practice due to inherent multicollinearity issues and the inadequacy of classical distinguishers, which lose discriminating power when targeting injective functions.
In this work, we overcome these limitations and propose the first fully generic-compatible non profiled attack. We show that using a Walsh-Hadamard basis enables generic LRA by eliminating multicollinearity and allowing all predictors to be considered without loss of precision. We also introduce new generic-compatible distinguishers tailored to LRA and formally prove their soundness for both linear and non-linear cryptographic operations. Finally, we validate our approach experimentally using simulations and publicly available datasets.