Name
Abstract | ||
|---|---|---|
•The distributed DLMS algorithm based on KL divergence (DLMSKL) is proposed to detect false data injection (FDI) attack.•Three different DLMSKL algorithms are proposed to weaken the impact of the continual and time-sharing FDI attack.•The proposed algorithms guarantee the stable convergence and maintain favorable robustness under FDI attack.•Mean and mean-square performance analysis of the three DLMSKL algorithms are provided. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ins.2019.12.016 | Information Sciences |
Keywords | Field | DocType |
False data injection attack,Distributed estimation,KL divergence,Time-sharing attack,Wireless sensor networks | Divergence,Computer science,Algorithm,Distributed algorithm,Step detection,Least mean square algorithm,Wireless sensor network,Kullback–Leibler divergence | Journal |
Volume | ISSN | Citations |
515 | 0020-0255 | 2 |
PageRank | References | Authors |
0.36 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Hua | 1 | 3 | 2.06 |
| Feng Chen | 2 | 17 | 5.08 |
| Shuwei Deng | 3 | 2 | 0.36 |
| Shukai Duan | 4 | 13 | 5.62 |
| Lidan Wang | 5 | 373 | 42.92 |