Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using RT-IoT2022 dataset

Abstract

In recent years, many researchers focused on unsupervised learning for network anomaly detection in edge devices to identify attacks. The deployment of the unsupervised autoencoder model is computationally expensive in resource-constrained edge devices. This study proposes quantized autoencoder (QAE) model for intrusion detection systems to detect anomalies. QAE is an optimization model derived from autoencoders that incorporate pruning, clustering, and integer quantization techniques. Quantized autoencoder uint8 (QAE-u8) and quantized autoencoder float16 (QAE-f16) are two variants of QAE built to deploy computationally expensive AI models into Edge devices. First, we have generated a Real-Time Internet of Things 2022 dataset for normal and attack traffic. The autoencoder model operates on normal traffic during the training phase. The same model is then used to reconstruct anomaly traffic under the assumption that the reconstruction error (RE) of the anomaly will be high, which helps to identify the attacks. Furthermore, we study the performance of the autoencoders, QAE-u8, and QAE-f16 using accuracy, precision, recall, and F1 score through an extensive experimental study. We showed that QAE-u8 outperforms all other models with a reduction of 70.01% in average memory utilization, 92.23% in memory size compression, and 27.94% in peak CPU utilization. Thus, the proposed QAE-u8 model is more suitable for deployment on resource-constrained IoT edge devices.