Intrusion detection of cyber physical energy system based on multivariate ensemble classification

The tight coupling of information and communication technology and traditional energy system has given birth to a cyber physical energy system (CPES). CPES indeed improves the economic operation and control efficiency of the energy system, but it also brings new cyber risk issues, threatening the secure operation of the energy system. Consequently, this paper proposes a new multivariate ensemble classification (MEC) method to detect intrusions in CPES, thereby enhancing the baseline cybersecurity of CPES. MEC simultaneously takes into account the detection accuracy, stability and computing efficiency. In MEC, extreme gradient boosting, light gradient boosting machine and extreme learning machine are separately designed as individual detectors for intrusion identification. Then, ensemble learning based decision-making is developed to strategically aggregate the results of all individual detectors. Finally, the effectiveness of the proposed MEC is validated on IEEE standard 14-, 57- and 118-bus systems. The obtained results demonstrate that the MEC method has an attractive potential in real applications.