Template Attack vs Bayes Classifier

Side-channel attacks represent one of the most powerful category of attacks on cryptographic devices with profiled attacks in a promi- nent place as the most powerful among them. Indeed, for instance, template attack is a well-known real-world attack that is also the most powerful attack from the information theoretic perspective. On the other hand, machine learning techniques have proven their quality in a numerous applications where one is definitely side-channel analysis. As one could expect, most of the research concerning supervised machine learning and side-channel analysis concentrated on more powerful machine learning techniques. Although valid from the practical perspective, such attacks often remain lacking from the more theoretical side. In this paper, we investigate several Bayes classifiers, which present simple supervised techniques that have significant similarities with the template attack. More specifically, our analysis aims to investigate what is the influence of the feature (in)dependence in datasets with different amount of noise and to offer further insight into the efficiency of machine learning for side-channel analysis

Performance comparison between deep learning-based and conventional cryptographic distinguishers

While many similarities between Machine Learning and cryptanalysis tasks exists, so far no major result in cryptanalysis has been reached with the aid of Machine Learning techniques. One exception is the recent work of Gohr, presented at Crypto 2019, where for the first time, conventional cryptanalysis was combined with the use of neural networks to build a more efficient distinguisher and, consequently, a key recovery attack on Speck32/64. On the same line, in this work we propose two Deep Learning (DL) based distinguishers against the Tiny Encryption Algorithm (TEA) and its evolution RAIDEN. Both ciphers have twice block and key size compared to Speck32/64. We show how these two distinguishers outperform a conventional statistical distinguisher, with no prior information on the cipher, and a differential distinguisher based on the differential trails presented by Biryukov and Velichkov at FSE 2014. We also present some variations of the DL-based distinguishers, discuss some of their extra features, and propose some directions for future research