SCADA Anomaly Detection Scheme Based on OCSVM-PSO

With the development of information technology and the digital transformation of the power system and chemical industry, such industrial control systems are becoming increasingly vulnerable to network attacks, and the deployment of traditional methods has shown shortcomings, especially in terms of resilience and adaptation to future diversification trends, so the deployment of highly efficient and accurate intrusion detection technology is crucial in the power system. In this paper, an intrusion detection model scheme based on machine learning optimization algorithms is proposed for the data collected by SCADA networks and network communication traffic in industrial control power systems. The intrusion detection model scheme for SCADA network mainly includes three modules: data set construction, machine learning intrusion detection algorithm and algorithm optimization: after the process of data collection and pre-processing, OCSVM is adopted as the main intrusion detection algorithm, which is suitable for the anomaly detection task, whereas in the algorithm optimization, PSO is used to adjust the parameters of OCSVM, which is used to improve the detection performance of the model. The final experimental data shows that the OCSVM-PSO model of this scheme has stronger potential and advantages in the field of power system intrusion detection compared to the OCSVM model.