| Title | Local Outlier Factor Based False Data Detection in Power Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Ou, Yifan, Deng, Bin, Liu, Xuan, Zhou, Ke |
| Conference Name | 2019 IEEE Sustainable Power and Energy Conference (iSPEC) |
| Date Published | nov |
| Keywords | anomaly detection, composability, cyber attackers, cyber physical systems, density clustering, density-clustering based LOF algorithm, dummy data attack, electric power delivery, false data attack, False Data Detection, Human Behavior, local outlier factor, pattern clustering, power engineering computing, power grids, Power systems, pubcrawl, resilience, Resiliency, security of data, Smart grids, smart power grids |
| Abstract | The rapid developments of smart grids provide multiple benefits to the delivery of electric power, but at the same time makes the power grids under the threat of cyber attackers. The transmitted data could be deliberately modified without triggering the alarm of bad data detection procedure. In order to ensure the stable operation of the power systems, it is extremely significant to develop effective abnormal detection algorithms against injected false data. In this paper, we introduce the density-based LOF algorithm to detect the false data and dummy data. The simulation results show that the traditional density-clustering based LOF algorithm can effectively identify FDA, but the detection performance on DDA is not satisfactory. Therefore, we propose the improved LOF algorithm to detect DDA by setting reasonable density threshold. |
| DOI | 10.1109/iSPEC48194.2019.8974951 |
| Citation Key | ou_local_2019 |
Local Outlier Factor Based False Data Detection in Power Systems