Distributed Joint Attack Detection and Secure State Estimation
Distributed Joint Attack Detection and Secure State Estimation
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DOI：
10.1109/tsipn.2017.2760804
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发表时间：
2018-03
影响因子：
3.2
通讯作者：
N. Forti;G. Battistelli;L. Chisci;Suqi Li;Bailu Wang;B. Sinopoli
中科院分区：
文献类型：
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作者：
N. Forti;G. Battistelli;L. Chisci;Suqi Li;Bailu Wang;B. Sinopoli
检测攻击和安全监控网络物理系统状态的联合任务是通过基于集群的网络来解决的，其中多个融合节点从传感器收集数据并以邻居方式合作以完成任务。攻击检测状态估计问题是在随机集理论的背景下，通过混合伯努利随机集（HBRS）密度表示攻击存在/不存在、系统状态和攻击信号的联合信息来制定的。然后，结合前人关于HBRS递归贝叶斯滤波的研究成果和关于HBRS的Kullback-Leibler平均的研究成果，提出了一种新的分布式HBRS滤波器，并以广域电网监测为例对其有效性进行了验证。
The joint task of detecting attacks and securely monitoring the state of a cyber-physical system is addressed over a cluster-based network wherein multiple fusion nodes collect data from sensors and cooperate in a neighborwise fashion in order to accomplish the task. The attack detection–state estimation problem is formulated in the context of random set theory by representing joint information on the attack presence/absence, on the system state, and on the attack signal in terms of a hybrid Bernoulli random set (HBRS) density. Then, combining previous results on HBRS recursive Bayesian filtering with novel results on Kullback–Leibler averaging of HBRSs, a novel distributed HBRS filter is developed and its effectiveness is tested on a case study concerning wide-area monitoring of a power network.