Open Access

REVIEW

Recent Advances in Deep-Learning Side-Channel Attacks on AES Implementations

Junnian Wang¹, Xiaoxia Wang¹, Zexin Luo¹, Qixiang Ouyang¹, Chao Zhou¹, Huanyu Wang^2,*

1 School of Physics and Electronic Science, Hunan University of Science and Technology, Xiangtan, 411201, China
2 School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

* Corresponding Author: Huanyu Wang. Email:

Computers, Materials & Continua 2026, 87(1), 3 https://doi.org/10.32604/cmc.2025.074473

Received 11 October 2025; Accepted 18 November 2025; Issue published 10 February 2026

Abstract

Internet of Things (IoTs) devices are bringing about a revolutionary change our society by enabling connectivity regardless of time and location. However, The extensive deployment of these devices also makes them attractive victims for the malicious actions of adversaries. Within the spectrum of existing threats, Side-Channel Attacks (SCAs) have established themselves as an effective way to compromise cryptographic implementations. These attacks exploit unintended, unintended physical leakage that occurs during the cryptographic execution of devices, bypassing the theoretical strength of the crypto design. In recent times, the advancement of deep learning has provided SCAs with a powerful ally. Well-trained deep-learning models demonstrate an exceptional capacity to identify correlations between side-channel measurements and sensitive data, thereby significantly enhancing such attacks. To further understand the security threats posed by deep-learning SCAs and to aid in formulating robust countermeasures in the future, this paper undertakes an exhaustive investigation of leading-edge SCAs targeting Advanced Encryption Standard (AES) implementations. The study specifically focuses on attacks that exploit power consumption and electromagnetic (EM) emissions as primary leakage sources, systematically evaluating the extent to which diverse deep learning techniques enhance SCAs across multiple critical dimensions. These dimensions include: (i) the characteristics of publicly available datasets derived from various hardware and software platforms; (ii) the formalization of leakage models tailored to different attack scenarios; (iii) the architectural suitability and performance of state-of-the-art deep learning models. Furthermore, the survey provides a systematic synthesis of current research findings, identifies significant unresolved issues in the existing literature and suggests promising directions for future work, including cross-device attack transferability and the impact of quantum-classical hybrid computing on side-channel security.

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