| Title | Dynamic Detection Model of False Data Injection Attack Facing Power Network Security |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Chang, Fuhong, Li, Qi, Wang, Yuanyuan, Zhang, Wenfeng |
| Conference Name | 2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) |
| Keywords | Data models, dynamic detection, False Data Injection, false trust, filtering algorithms, Heuristic algorithms, Network security, Nonlinear dynamical systems, policy-based governance, power grid system, Power system dynamics, pubcrawl, resilience, Resiliency, Scalability, Seminars, state evaluation |
| Abstract | In order to protect the safety of power grid, improve the early warning precision of false data injection. This paper presents a dynamic detection model for false data injection attacks. Based on the characteristics of APT attacks, a model of attack characteristics for trusted regions is constructed. In order to realize the accurate state estimation, unscented Kalman filtering algorithm is used to estimate the state of nonlinear power system and realize dynamic attack detection. Experimental results show that the precision of this method is higher than 90%, which verifies the effectiveness of this paper in attack detection. |
| DOI | 10.1109/AINIT54228.2021.00069 |
| Citation Key | chang_dynamic_2021 |
Dynamic Detection Model of False Data Injection Attack Facing Power Network Security