Paper 2025/1754

Machine Learning and Side-Channel Attacks on Post-Quantum Cryptography

Abstract

The transition to post-quantum cryptography (PQC) is accelerating due to the potential of quantum computing to compromise classical public-key cryptosystems. While standardized schemes such as CRYSTALS-Kyber, CRYSTALS-Dilithium, and SPHINCS+ offer strong theoretical security, practical deployments remain susceptible to physical-layer vulnerabilities, notably side-channel attacks (SCAs). SCAs exploit unintentional leakages in hardware and software implementations—such as power traces, electromagnetic emissions, and timing variations—to recover secret keys without altering the target system. These attacks are non-invasive, cost-effective, and applicable across diverse platforms, making them a critical threat vector for PQC in embedded and resource-constrained environments. This survey provides a structured, in-depth review of SCAs targeting PQC implementations, encompassing both classical methods—such as Simple Power Analysis, Differential Power Analysis, Correlation Power Analysis, Template Attacks, and Mutual Information Analysis—and emerging machine learning (ML)-driven approaches. Special attention is given to deep learning models, including CNNs, RNNs, and MLPs, which have demonstrated superior performance in profiling attacks by automatically learning leakage patterns from high-dimensional trace data, even in the presence of countermeasures like masking and desynchronization. We categorize and compare recent attack strategies, analyze their effectiveness against various PQC schemes, and examine the limitations of existing countermeasures. Finally, we identify open research challenges and outline hybrid defense strategies that integrate classical protections with adaptive, ML-aware mitigation techniques. This comprehensive synthesis aims to bridge the gap between PQC algorithm design and secure, implementation-level deployment in the quantum era.