An Efficient Intrusion Detection Model for Edge System in Brownfield Industrial Internet of Things

The Industrial Internet of Things (IIoT) is bringing control systems online leading to significant innovation in industry and business. However, this development also comes with new cybersecurity threats. As much of the value of IIoT systems resides at the edge tier, this makes them potentially desired targets for attackers. Protecting edge physical systems by monitoring them and identifying malicious activities based on an efficient detection model is therefore of utmost importance. This paper proposes a detection model based on deep learning techniques that can learn and test using data collected from Remote Telemetry Unit (RTU) streams of gas pipeline system. It utilizes the sparse and denoising auto-encoder methods for unsupervised learning and deep neural networks for supervised learning to produce a high-level data representation from unlabeled and noisy data. Our results show that the proposed model achieves superior performance in identifying malicious activities.