Title: Security Defense of Large Scale Networks Under False Data Injection Attacks: An Attack Detection Scheduling Approach Show Chinese title

Title: 大规模网络在虚假数据注入攻击下的安全防御：一种攻击检测调度方法

Abstract: In large-scale networks, communication links between nodes are easily injected with false data by adversaries. This paper proposes a novel security defense strategy from the perspective of attack detection scheduling to ensure the security of the network. Based on the proposed strategy, each sensor can directly exclude suspicious sensors from its neighboring set. First, the problem of selecting suspicious sensors is formulated as a combinatorial optimization problem, which is non-deterministic polynomial-time hard (NP-hard). To solve this problem, the original function is transformed into a submodular function. Then, we propose a distributed attack detection scheduling algorithm based on the sequential submodular optimization theory, which incorporates \emph{expert problem} to better utilize historical information to guide the sensor selection task at the current moment. For different attack strategies, theoretical results show that the average optimization rate of the proposed algorithm has a lower bound, and the error expectation for any subset is bounded. In addition, under two kinds of insecurity conditions, the proposed algorithm can guarantee the security of the entire network from the perspective of the augmented estimation error. Finally, the effectiveness of the proposed method is verified by the numerical simulation and practical experiment. Show Chinese abstract

Abstract: 在大规模网络中，节点之间的通信链路容易被对手注入虚假数据。 本文从攻击检测调度的角度提出了一种新颖的安全防御策略，以确保网络的安全性。 基于该策略，每个传感器可以直接将其邻近集合中的可疑传感器排除出去。 首先，选择可疑传感器的问题被形式化为一个组合优化问题，这是非确定性多项式时间难（NP-hard）问题。 为了解决这个问题，原始函数被转换为一个子模函数。 然后，我们提出了一种基于顺序子模优化理论的分布式攻击检测调度算法，该算法结合了\emph{专家问题}以更好地利用历史信息来指导当前时刻的传感器选择任务。 对于不同的攻击策略，理论结果表明所提出的算法的平均优化率有一个下界，并且任何子集的误差期望是有限的。 此外，在两种不安全条件下，所提出的算法可以从增强估计误差的角度保证整个网络的安全性。 最后，通过数值仿真和实际实验验证了所提出方法的有效性。