On the optimality and practicability of mutual information analysis in some scenarios
Abstract
The best possible side-channel attack maximizes the success rate and would correspond to a maximum likelihood distinguisher if the leak- age probabilities were totally known or accurately estimated in a profiling phase. When profiling is unavailable, however, it is not clear whether Mutual Information Analysis (MIA), Correlation Power Analysis (CPA), or Linear Regression Analysis (LRA) would be the most successful in a given scenario. In this paper, we show that MIA coincides with the maximum likelihood expression when leakage probabilities are replaced by online estimated prob- abilities.
We then exhibit two case-studies where MIA outperforms CPA. One case is when the leakage model is known but the noise is not Gaussian. The second case is when the leakage model is partially unknown and the noise is Gaussian. In the latter scenario MIA is more efficient than LRA of any order.
Domains
Computer Science [cs] Cryptography and Security [cs.CR] Mathematics [math] Information Theory [math.IT] Computer Science [cs] Discrete Mathematics [cs.DM] Computer Science [cs] Human-Computer Interaction [cs.HC] Computer Science [cs] Signal and Image Processing Engineering Sciences [physics] Signal and Image processing Mathematics [math] Statistics [math.ST] Mathematics [math] Probability [math.PR] Mathematics [math] Functional Analysis [math.FA] Mathematics [math] Classical Analysis and ODEs [math.CA] Mathematics [math] General Mathematics [math.GM] Computer Science [cs] Information Theory [cs.IT]
Origin : Files produced by the author(s)