Title: Stealth Data Injection Attacks with Sparsity Constraints Show Chinese title

Title: 具有稀疏性约束的隐身数据注入攻击

Abstract: Sparse stealth attack constructions that minimize the mutual information between the state variables and the observations are proposed. The attack construction is formulated as the design of a multivariate Gaussian distribution that aims to minimize the mutual information while limiting the Kullback-Leibler divergence between the distribution of the observations under attack and the distribution of the observations without attack. The sparsity constraint is incorporated as a support constraint of the attack distribution. Two heuristic greedy algorithms for the attack construction are proposed. The first algorithm assumes that the attack vector consists of independent entries, and therefore, requires no communication between different attacked locations. The second algorithm considers correlation between the attack vector entries which results in better attack performance at the expense of coordination between different locations. We numerically evaluate the performance of the proposed attack constructions on IEEE test systems and show that it is feasible to construct stealth attacks that generate significant disruption with a low number of compromised sensors. Show Chinese abstract

Abstract: 提出了一种稀疏隐蔽攻击构造方法，旨在最小化状态变量和观测值之间的互信息。 攻击构造被表述为设计一个多变量高斯分布，目标是最小化互信息，同时限制受攻击情况下观测值的分布与未受攻击情况下观测值的分布之间的 Kullback-Leibler 散度。 稀疏性约束被纳入攻击分布的支持约束中。 提出了两种用于攻击构造的启发式贪心算法。 第一种算法假设攻击向量由独立的条目组成，因此不需要不同攻击位置之间的通信。 第二种算法考虑了攻击向量条目之间的相关性，这以不同位置之间需要协调为代价，获得了更好的攻击性能。 我们在 IEEE 测试系统上数值评估了所提出的攻击构造的性能，并表明可以构建出能够用较少数量的受损传感器造成显著干扰的隐蔽攻击。