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

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…

• NN
  National Natural Science Foundation of China

  Awards: 61972288, 62403122, 92067108
• NS
  Natural Science Foundation of Guangdong Province

  Award: 2021A151501131