Evolutionary Adversarial Autoencoder for Unsupervised Anomaly Detection of Industrial Internet of Things

Abstract

The rapid growth of interconnected smart devices and advanced computing technologies in the industrial Internet of Things (IIoT) has significantly enhanced operational resilience and performance but also increased cybersecurity risks. While deep learning shows promise in IIoT security, it faces challenges due to the lack of labeled data and reliance on human expertise for unsupervised anomaly detection. To address these challenges, a novel automated adversarial deep learning-based unsupervised anomaly detection method called EvoAAE is proposed to optimize the hyperparameters and neural architectures of adversarial variational autoencoder (VAE) for securing IIoT. Specifically, a generative adversarial network-based VAE is employed to adversarially generate multivariate time series. Then, particle swarm optimization with an efficient binary encoding strategy is designed to evolve hyperparameters and neural architectures in adversarial VAE including batch size, learning rate, the type of optimizer, the number of convolutional layer, the number of kernels of convolutional layer, kernel size, the type of normalization layer, and the type of active function. The experimental results indicate that EvoAAE achieves notable performance across four IIoT datasets in industrial control domain, i.e., secure water treatment, water distribution, Mars Science Laboratory, and power system domain, i.e., power system attack with precision of 0.949, 0.8356, 0.972, and 0.981, recall of 0.971, 0.9214, 0.964, and 0.979, and $F_{1}$-score of 0.960, 0.8764, 0.968, and 0.980, respectively.