Anomaly detection for industrial control operations with optimized ABC–SVM and weighted function code correlation analysis

Abstract

Under the tendency of interconnection and interoperability in Industrial Internet, anomaly detection, which has been widely recognized, has won significant accomplishments in industrial cyber security. However, a crucial issue is how to effectively extract industrial communication features which can accurately and comprehensively describe industrial control operations. Aiming at the function code field in industrial Modbus/TCP communication protocol, this paper proposes a novel feature extraction algorithm based on weighted function code correlation, which not only indicates the contribution of single function code in the whole function code sequence, but also analyzes the correlation of different function codes. In order to design a serviceable detection engine, a dynamic adjusting ABC–SVM (Artificial Bee Colony–Support Vector Machine) anomaly detection model based on double mutations is also developed to identify abnormal behaviors in industrial control communications. The experimental results show that the proposed feature extraction algorithm can effectively reflect the changes of function control behavior in industrial control communications, and the improved ABC–SVM anomaly detection model can strengthen the detection performance by comparing with other anomaly detection engines.