| Title | Isolation Forest based Detection for False Data Attacks in Power Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Song, Yufei, Yu, Zongchao, Liu, Xuan, Tian, Jianwei, CHEN, Mu |
| Conference Name | 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) |
| Date Published | may |
| Keywords | bad data detection, composability, Cyber Attacks, cyber physical systems, cyberattack, entire power grid, false data attack, False Data Detection, false data injection attacks, fault diagnosis, Forestry, Human Behavior, IEEE 118-bus system, IF based detection method, integrated communication networks, isolation forest, isolation forest based detection algorithm, network information, Outlier detection, power system security, Power systems, pubcrawl, real-time data, Real-time Systems, resilience, Resiliency, Smart grids, state estimation, Transmission line measurements, Vegetation |
| Abstract | Power systems become a primary target of cyber attacks because of the vulnerability of the integrated communication networks. An attacker is able to manipulate the integrity of real-time data by maliciously modifying the readings of meters transmitted to the control center. Moreover, it is demonstrated that such attack can escape the bad data detection in state estimation if the topology and network information of the entire power grid is known to the attacker. In this paper, we propose an isolation forest (IF) based detection algorithm as a countermeasure against false data attack (FDA). This method requires no tedious pre-training procedure to obtain the labels of outliers. In addition, comparing with other algorithms, the IF based detection method can find the outliers quickly. The performance of the proposed detection method is verified using the simulation results on the IEEE 118-bus system. |
| DOI | 10.1109/ISGT-Asia.2019.8881319 |
| Citation Key | song_isolation_2019 |
Isolation Forest based Detection for False Data Attacks in Power Systems