Industrial Anomaly Detection and Attack Classification Method Based on Convolutional Neural Network

Abstract

The massive use of information technology has brought certain security risks to the industrial production process. In recent years, cyber-physical attacks against industrial control systems have occurred frequently. Anomaly detection technology is an essential technical means to ensure the safety of industrial control systems. Considering the shortcomings of traditional methods and to facilitate the timely analysis and location of anomalies, this study proposes a solution based on the deep learning method for industrial traffic anomaly detection and attack classification. We use a convolutional neural network deep learning representation model as the detection model. The original one-dimensional data are mapped using the feature mapping method to make them suitable for model processing. The deep learning method can automatically extract critical features and achieve accurate attack classification. We performed a model evaluation using real network attack data from a supervisory control and data acquisition (SCADA) system. The experimental results showed that the proposed method met the anomaly detection and attack classification needs of a SCADA system. The proposed method also promotes the application of deep learning methods in industrial anomaly detection.