School of Electronic Engineering, Heilongjiang University, Harbin 150080, China.
Sensors (Basel). 2022 Dec 28;23(1):335. doi: 10.3390/s23010335.
In this study, distributed security estimation problems for networked stochastic uncertain systems subject to stochastic deception attacks are investigated. In sensor networks, the measurement data of sensor nodes may be attacked maliciously in the process of data exchange between sensors. When the attack rates and noise variances for the stochastic deception attack signals are known, many measurement data received from neighbour nodes are compressed by a weighted measurement fusion algorithm based on the least-squares method at each sensor node. A distributed optimal filter in the linear minimum variance criterion is presented based on compressed measurement data. It has the same estimation accuracy as and lower computational cost than that based on uncompressed measurement data. When the attack rates and noise variances of the stochastic deception attack signals are unknown, a correlation function method is employed to identify them. Then, a distributed self-tuning filter is obtained by substituting the identified results into the distributed optimal filtering algorithm. The convergence of the presented algorithms is analyzed. A simulation example verifies the effectiveness of the proposed algorithms.
本研究针对遭受随机欺骗攻击的网络随机不确定系统的分布式安全估计问题展开研究。在传感器网络中，传感器节点的测量数据在传感器之间进行数据交换的过程中可能会受到恶意攻击。当随机欺骗攻击信号的攻击率和噪声方差已知时，每个传感器节点会根据最小二乘法的加权测量融合算法对来自邻居节点的多个测量数据进行压缩。在此基础上，基于压缩测量数据，提出了一种线性最小方差准则下的分布式最优滤波器。与基于未压缩测量数据的滤波器相比，它具有相同的估计精度和更低的计算成本。当随机欺骗攻击信号的攻击率和噪声方差未知时，采用相关函数法对其进行辨识。然后，将辨识结果代入分布式最优滤波算法，得到分布式自校正滤波器。分析了所提出算法的收敛性。仿真示例验证了所提算法的有效性。
