Detection, estimation, and compensation of false data injection attack for UAVs

被引:50
|
作者
Gu, Yapei [1 ]
Yu, Xiang [1 ,2 ]
Guo, Kexin [1 ]
Qiao, Jianzhong [1 ,2 ]
Guo, Lei [1 ,2 ]
机构
[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
[2] Beihang Univ, Big Data Based Precis Med, Beijing 100191, Peoples R China
基金
中国国家自然科学基金;
关键词
Unmanned aerial vehicle; False data injection attack; Attack modeling; Attack detection; Attack estimation;
D O I
10.1016/j.ins.2020.08.055
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The safety issues of unmanned aerial vehicles (UAVs) are ever increasing in focus due to the vulnerability to attack. This paper investigates the safety problem for UAVs under the false data injection attack (FDIA) through wireless data link. First, the attack characteristics and influencing factors of FDIA are explicitly analyzed. Thus, the models of FDIA can be established. Second, attack detection mechanism is proposed. The average power of received signals and the residual of the authentication signals are utilized to detect whether the system is attacked. Third, to compensate the effect of FDIA on the control system, the FDIA impact on UAV dynamics is formulated and attack observers are developed accordingly. Finally, simulation examples are presented to illustrate the effectiveness of the proposed methods. (C) 2020 Elsevier Inc. All rights reserved.
引用
收藏
页码:723 / 741
页数:19
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