Enhancing IoT Security with a Hardware Accelerated Machine Learning Model coupling Autoencoder and Long-Short-Term-Memory for Anomaly Detection

Kuchul Jung, Jongseok Woo, Saibal Mukhopadhyay

Abstract: This paper proposes a hardware accelerator for machine learning-based anomaly detection to enhance IoT security. Our model integrates Multilayer Perceptron (MLP) with Long Short-Term Memory (LSTM), utilizing an MLP-based Autoencoder and Isolation Forest algorithm for data dimensionality reduction and computational complexity reduction. Prototyped on a Zynq UltraScale+ XCZU9EG FPGA, our AE-LSTM model surpasses baseline MLP-only and LSTM-only models in resource utilization efficiency and detection accuracy. Compared to these baselines, it reduces parameters by 79.4% and 98% and LUT usage by 61.4% and 90.8%, respectively, while minimizing other resource utilization. Furthermore, power consumption is lowered to about 40% of the MLP-based model's consumption rate and 36% of the LSTM-based model's rate, with latency reduced to less than one-third from both baselines.