{"ID":2897783,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.04197","arxiv_id":"2507.04197","title":"ML-Enhanced AES Anomaly Detection for Real-Time Embedded Security","abstract":"Advanced Encryption Standard (AES) is a widely adopted cryptographic algorithm, yet its practical implementations remain susceptible to side-channel and fault injection attacks. In this work, we propose a comprehensive framework that enhances AES-128 encryption security through controlled anomaly injection and real-time anomaly detection using both statistical and machine learning (ML) methods. We simulate timing and fault-based anomalies by injecting execution delays and ciphertext perturbations during encryption, generating labeled datasets for detection model training. Two complementary detection mechanisms are developed: a threshold-based timing anomaly detector and a supervised Random Forest classifier trained on combined timing and ciphertext features. We implement and evaluate the framework on both CPU and FPGA-based SoC hardware (PYNQ-Z1), measuring performance across varying block sizes, injection rates, and core counts. Our results show that ML-based detection significantly outperforms threshold-based methods in precision and recall while maintaining real-time performance on embedded hardware. Compared to existing AES anomaly detection methods, our solution offers a low-cost, real-time, and accurate detection approach deployable on lightweight FPGA platforms.","short_abstract":"Advanced Encryption Standard (AES) is a widely adopted cryptographic algorithm, yet its practical implementations remain susceptible to side-channel and fault injection attacks. In this work, we propose a comprehensive framework that enhances AES-128 encryption security through controlled anomaly injection and real-tim...","url_abs":"https://arxiv.org/abs/2507.04197","url_pdf":"https://arxiv.org/pdf/2507.04197v1","authors":"[\"Nishant Chinnasami\",\"Rye Stahle-Smith\",\"Rasha Karakchi\"]","published":"2025-07-06T00:22:58Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[]","has_code":false}