Lightweight Deep Learning Optimization of AES for Mobile IoT Security

Abstract

Mobile Internet of Things (IoT) terminals are typically constrained by limited computing power, memory, and battery life, making traditional implementations of the Advanced Encryption Standard (AES) suffer from excessive computational overhead, high latency, and insufficient resistance to side-channel attacks. To address these key limitations, this paper proposes a lightweight deep learning-based AES optimization scheme tailored for resource-constrained environments. We designed a lightweight Convolutional Neural Network (CNN) based on MobileNet depthwise separable convolutions to reconstruct the SubBytes transformation, and a lightweight Long Short-Term Memory (LSTM) network enhanced with structured pruning and 8-bit quantization to optimize the key expansion module. This approach significantly enhances the nonlinearity of encryption operations and the randomness of round keys while reducing model parameters and computational load. Extensive experiments on the ARM Cortex-M4 chip demonstrate that the optimized AES achieves a 38% improvement in encryption throughput and a 42% reduction in key expansion time. Security evaluations show that the round key Shannon entropy increases to 148.2 bits, with substantially enhanced resistance to differential attacks, linear attacks, and Differential Power Analysis (DPA, with Correlation Power Analysis [CPA] as its mainstream engineering implementation). Notably, the optimized algorithm only increases on-chip memory usage by 12% and reduces static power consumption by 18%, making it suitable for deployment on low-resource mobile IoT devices.