Advance attacks on AES: A comprehensive review of side channel, fault injection, machine learning and quantum techniques

Shiraz Naserelden (), Norma Alias (), Abdelrahamn Altigani (), Ahmed Mohamed () and Said Badreddine ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 4, 2471-2486

Abstract: The Advanced Encryption Standard (AES) remains a foundational component of modern cryptography, securing vast volumes of digital communication and data storage. Despite its robust design and widespread adoption, AES continues to be the subject of intensive cryptanalytic research. This paper presents a review of recent advances in attacks against AES, categorizing them into four domains: side-channel attacks, fault injection attacks, machine learning and AI-based attacks, and quantum computing threats. For each category, representative studies published between 2021 and 2025 are analyzed with respect to methodology, data requirements, attack complexity, and practical applicability. The review highlights both vulnerabilities exposed in specific AES implementations and the evolution of attack methodologies, thereby providing a comprehensive perspective on the contemporary threat landscape. The findings underscore the need for continuous evaluation and adaptation of AES-based systems to ensure cryptographic resilience in the face of advancing adversarial capabilities.

Keywords: Advanced encryption standard (AES); AES security; Side-channel attacks; Fault injection; Machine learning in cryptanalysis; Quantum cryptanalysis (search for similar items in EconPapers)
Date: 2025
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