ML-Enhanced AES Anomaly Detection for Real-Time Embedded Security

• URL: http://arxiv.org/abs/2507.04197v1
• Date: Sun, 06 Jul 2025 00:22:58 GMT
• Title: ML-Enhanced AES Anomaly Detection for Real-Time Embedded Security
• Authors: Nishant Chinnasami, Rye Stahle-Smith, Rasha Karakchi,
• Abstract summary: We propose a comprehensive framework that enhances AES-128 encryption security through controlled anomaly injection and real-time anomaly detection.<n>We simulate timing and fault-based anomalies by injecting execution delays and ciphertext perturbations during encryption, generating labeled datasets for detection model training.<n>Our results show that ML-based detection significantly outperforms threshold-based methods in precision and recall while maintaining real-time performance on embedded hardware.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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