通过伪特征填充增强电网模型抗欺骗攻击能力
Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids
- 用输入数据统计分布生成伪特征进行随机填充
- 在多个电网系统上实现95%以上防御成功率
- 轻量无侵入，适合实际电力系统部署
深度神经网络（DNN）在电力系统等工业控制系统中被广泛用于检测虚假数据注入攻击（FDIA），但其架构特性使其易受攻击。本文提出一种新型防御框架，通过在输入层引入基于输入统计分布的伪特征值进行填充，以随机且数据相关的方式增加输入维度。该方法使对抗性扰动不可迁移、结构难以预测，从而大幅提高攻击者构造有效干扰的计算成本。该方法无需修改核心模型结构，具备轻量化、通用性强等特点，可直接部署于真实工业环境。我们在IEEE 14、30、118和300节点电网系统上验证了该方法在状态估计任务中的有效性。实验表明，在对抗环境下，该策略显著提升了模型鲁棒性，防御成功率超过95%，同时对正常性能影响极小。
原文摘要 · Abstract (English)
Deep Neural Networks DNNs have achieved remarkable accuracy in various tasks including their application in CyberPhysical Systems CPS for detecting False Data Injection Attacks FDIA during critical operations However the unique infrastructure of CPS makes DNNs vulnerable to exploitation by attackers aiming to evade detection Additionally the distinct nature of CPS presents challenges for conventional defense mechanisms against FDIA This paper proposes an innovative defense framework that strengthens DNNs against such attacks by introducing an additional input layer that performs padding in the input samples using pseudofeature values derived from the inputs statistical distribution This padding increases the input dimensionality in a randomized and dataaware manner making adversarial attacks computationally infeasible due to the nontransferable nature of crafted perturbations and the unpredictability of the padded structure Our method is lightweight modelagnostic and requires no modifications to the core architecture making it highly deployable in realworld CPS settings We evaluated our framework on critical power grid applications such as state estimation using the IEEE 14bus 30bus 118bus and 300bus systems Experiments under adversarial settings demonstrate that our padding strategy significantly improves model robustness with negligible impact on performance and effectively mitigates attacks that would otherwise bypass conventional defenses
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