Machine learning in side-channel analysis: a first study

By Gabriel Hospodar, Benedikt Gierlichs, E. D. Mulder, Ingrid M. R. Verbauwhede, Joos Vandewalle. Journal of Cryptographic Engineering, 2011

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Convolutional Neural Networks with Data Augmentation Against Jitter-Based Countermeasures - Profiling Attacks Without Pre-processing (2017, background): “Recently the strict analogy between template attacks and the supervised classification problem as been depicted [16], while the deployment of Neural Networks (NNs) [24, 23, 35] and CNNs [21] to perform profiled SCAs has been proposed.”
Dynamic Malware Analysis in the Modern Era—A State of the Art Survey (2019, background): “Machine learning in side-channel analysis: a first study [97] Hospodar, 2011 Cryptanalysis Attacking cryptographic devices with side-channel analysis is based on the statistic behavior of these devices.”
Breaking Cryptographic Implementations Using Deep Learning Techniques (2016, background): “A recent line of works has investigated new profiling attacks based on Machine Learning (ML) techniques to defeat both unprotected [5,23,28,32,34] and protected cryptographic implementations [21,33].”