稀疏约束下的信息论数据注入攻击
Information Theoretic Data Injection Attacks with Sparsity Constraints
摘要：
在贝叶斯状态估计环境下研究了同时最小化由操作员获得的信息和检测概率的信息论稀疏攻击。攻击构造被制定为一个优化问题，旨在最小化状态变量和观测值之间的互信息，同时保证攻击的隐身性。隐形是描述在攻击下和没有攻击的观测值的分布之间的Kullback-Leibler（KL）分歧。为了克服稀疏攻击构造的组合性质所带来的困难，首先解析地解决只有一个传感器受到损害的攻击情况。在这种情况下产生的洞察力，然后用来提出一个贪婪的算法，构建随机稀疏攻击。在IEEE 30总线测试用例中评估了所提出的攻击的性能。
英文摘要：
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.
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