Scalable anomaly-based intrusion detection for secure Internet of Things using generative adversarial networks in fog environment

Abstract

The data generated exponentially by a massive number of devices in the Internet of Things (IoT) are extremely high-dimensional, large-scale, non-labeled, which poses great challenges to timely analysis and effective decision making for anomaly detection in IoT. In this paper, we propose a novel unsupervised deep learning method to identify anomalies against IoT networks, which exploits Bidirectional Generative Adversarial Networks (BiGAN) to build model on normal IoT data. The model introduces Wasserstein distance to capture and learn the distribution of high-dimensional raw data and focuses on latent representations using an auxiliary classifier. A cycle consistency connection between data is designed to prevent information loss that helps to reduce false positive rate. The model detects outliers by utilizing reconstruction error in feature space. Another challenge facing the current anomaly detection solutions is their limited scalability, which restricts capability in handling big IoT data. This issue is resolved by deploying and jointly training the proposed method in a fog computing environment. The anomaly-based intrusion detection can be scalable by leveraging the flexibility of fog computing, which contributes to supporting efficient detection. Experimental results on two recent datasets (i.e., UNSW-NB15 and CIC-IDS2017) validate that the proposed method achieves 4% increase in accuracy and 4% reduction in false alarm rate than the state-of-the-art methods while keeping computational efficiency.