Machine Learning Techniques for Cryptographic Attack Detection

Cryptographic systems are essential for securing digital communications; however, increasing adversarial sophistication threatens their reliability and confidentiality. Machine Learning (ML) offers adaptive mechanisms for detecting cryptographic attacks by identifying anomalies, side-channel leakages, ciphertext irregularities, and protocol misuse patterns. This paper presents a comprehensive review and comparative analysis of ML models—supervised learning, deep learning, unsupervised learning, and reinforcement learning—applied to cryptographic attack detection. We evaluate public datasets, feature engineering methods, and detection pipelines, supplemented with diagrams and performance tables. Major challenges such as adversarial ML, data scarcity, and resource limitations are analyzed. The study concludes with future research directions to strengthen ML-assisted cryptographic security.