Industrial Control Intrusion Detection Approach Based on Multiclassification GoogLeNet-LSTM Model

Abstract

Intrusion detection is essential for ensuring the security of industrial control systems. However, conventional intrusion detection approaches are unable to cope with the complexity and ever-changing nature of industrial intrusion attacks. In this study, we propose an industrial control intrusion detection approach based on a combined deep learning model for communication processes that use the Modbus protocol. Initially, the network packets are classified as carrying information and noncarrying information based on key fields according to the communication protocol used. Next, a template comparison approach is employed to detect the network packets that do not carry any information. Furthermore, an approach based on a GoogLeNet-long short-term memory model is used to detect the network packets that do carry information. This approach involves network packet sequence construction, feature extraction, and time-series level detection. Subsequently, the detected intrusions are classified into multiple categories through a Softmax classifier. A gas pipeline dataset of the Modbus protocol is used to evaluate the proposed approach and compare it with existing strategies. The accuracy, false-positive rate, and miss rate are 97.56%, 2.42%, and 2.51%, respectively, thus confirming that the proposed approach is suitable for intrusion detection in industrial control systems.