论文标题
信息理论数据注射攻击具有稀疏性约束
Information Theoretic Data Injection Attacks with Sparsity Constraints
论文作者
论文摘要
在贝叶斯状态估计设置中研究了信息理论稀疏攻击，从而使操作员获得的信息和检测概率同时最小化。攻击构建被提出为优化问题，旨在最大程度地减少状态变量与观测值之间的相互信息，同时保证攻击的隐身。隐形是用kullback-leibler（KL）差异来描述的，在攻击和没有攻击的情况下的分布之间存在差异。为了克服稀疏攻击构建的组合性质所带来的困难，首先对只有一个传感器遭到损害的攻击案例进行了分析解决。然后，在这种情况下生成的见解被用来提出一种构建随机稀疏攻击的贪婪算法。在IEEE 30总线测试案例中评估了拟议攻击的性能。
Information theoretic sparse attacks that minimize simultaneously the information obtained by the operator and the probability of detection are studied in a Bayesian state estimation setting. The attack construction is formulated as an optimization problem that aims to minimize the mutual information between the state variables and the observations while guaranteeing the stealth of the attack. Stealth is described in terms of the Kullback-Leibler (KL) divergence between the distributions of the observations under attack and without attack. To overcome the difficulty posed by the combinatorial nature of a sparse attack construction, the attack case in which only one sensor is compromised is analytically solved first. The insight generated in this case is then used to propose a greedy algorithm that constructs random sparse attacks. The performance of the proposed attack is evaluated in the IEEE 30 Bus Test Case.
