Machine Learning-Based Similarity Attacks for Chaos-based Cryptosystems

Abstract

When the chaotic block cryptographic algorithms are performed on hardware devices, the leakages of power consumption etc. are crucial information which can be used to analyse the security of the cryptosystems. Template Attack (TA) can recover the secret key. However, there are still some challenges for TA such as irreversible covariance matrix and exponentiation calculation overflow. Machine Learning-based Similarity Attacks (MLSAs) are proposed to effectively analyse the sensitive information of the chaotic block cryptosystem. The proposed method consists of three steps: parameter tuning, learning and attacking. For the parameter tuning, the profiling traces are categorised according to the Hamming weights of sensitive intermediate data. Then a 10-fold cross-validation is executed to determine the corresponding parameter settings for learning algorithms. In the learning step, the profiling traces and Hamming weight labels are used to train machine learning models, and in the attacking step different similarity measure methods are used to calculate similarities between actual and hypothetical Hamming weight labels to attack the secret keys. Performance analyses demonstrate that the proposed MLSAs have higher success rates than TA and lower computational time consumptions under most of scenarios. Therefore, the MLSAs can efficiently attack and analyse hardware security of chaotic block cryptosystems.