| Title | A Data Driven Threat-Maximizing False Data Injection Attack Detection Method with Spatio-Temporal Correlation |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zhang, Guangdou, Li, Jian, Bamisile, Olusola, Zhang, Zhenyuan, Cai, Dongsheng, Huang, Qi |
| Conference Name | 2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) |
| Date Published | jul |
| Keywords | composability, Correlation, cyber-physical system (CPS), Cyber-physical systems, data-driven, Deep Convolutional Neural Network (DCNN), False Data Detection, false data injection (FDIA), feature extraction, Fluctuations, Human Behavior, power system stability, pubcrawl, renewable energy sources, resilience, Resiliency, Spatio-temporal correlation (STC), Voltage fluctuations |
| Abstract | As a typical cyber-physical system, the power system utilizes advanced information and communication technologies to transmit crucial control signals in communication channels. However, many adversaries can construct false data injection attacks (FDIA) to circumvent traditional bad data detection and break the stability of the power grid. In this paper, we proposed a threat-maximizing FDIA model from the view of attackers. The proposed FDIA can not only circumvent bad data detection but can also cause a terrible fluctuation in the power system. Furthermore, in order to eliminate potential attack threats, the Spatio-temporal correlations of measurement matrices are considered. To extract the Spatio-temporal features, a data-driven detection method using a deep convolutional neural network was proposed. The effectiveness of the proposed FDIA model and detection are assessed by a simulation on the New England 39 bus system. The results show that the FDIA can cause a negative effect on the power system's stable operation. Besides, the results reveal that the proposed FDIA detection method has an outstanding performance on Spatio-temporal features extraction and FDIA recognition. |
| DOI | 10.1109/ICPSAsia52756.2021.9621453 |
| Citation Key | zhang_data_2021 |
A Data Driven Threat-Maximizing False Data Injection Attack Detection Method with Spatio-Temporal Correlation