Machine-learning assisted side-channel attacks on RNS ECC implementations using hybrid feature engineering

Machine learning-based side-channel attacks have recently been introduced to recover the secret information from protected software and hardware implementations. Limited research exists for public-key algorithms, especially on non-traditional implementations like those using Residue Number System (RNS). Template attacks were proven successful on RNS-based Elliptic Curve Cryptography (ECC), only if the aligned portion is used for templates. In this study, we present a systematic methodology for the evaluation of ECC cryptosystems with and without countermeasures (both RNS-based and traditional ones) against ML-based side-channel attacks using two attack models on full length aligned and unaligned leakages. RNS-based ECC datasets are evaluated using four machine learning classifiers and comparison is provided with existing state-of-the-art template attacks. Moreover, we analyze the impact of raw features and advanced hybrid feature engineering techniques. We discuss the metrics and procedures that can be used for accurate classification on the imbalanced datasets. The experimental results demonstrate that, for ECC RNS datasets, the efficiency of simple machine learning algorithms is better than the complex deep learning techniques when such datasets are limited in size. This is the first study presenting a complete methodology for ML side-channel attacks on public key algorithms.