AI 中文总结
针对数据拒绝攻击使电网标准数据驱动检测方法失效的问题，提出模块化神经网络AdaptoNet，通过条件控制适配测量可用性，在四组IEEE测试系统上大幅提升检测F1分数。
AI 中文摘要
针对电网的网络攻击结合了物理干扰与受损数据，以破坏网络物理系统的稳定性。研究表明，数据拒绝攻击（攻击者在目标区域阻断测量值同时触发线路停运）会使检测性能降低超过86%，导致标准数据驱动方法失效。本文提出AdaptoNet，一种通过条件控制适应测量可用性的模块化神经网络，它将在完整数据上训练的冻结基础模块与基于二元测量可用性向量调节的可训练自适应模块配对，无需重新训练基础模块即可区分被拒绝数据与异常数据。在四个IEEE测试系统（30、39、57、118节点）上，针对阻断最多20%测量值的区域内攻击进行评估，AdaptoNet将F1分数从低于12%恢复至高于81%，提升约7倍，接近完整测量下89%-99%的基线水平。
英文摘要
Cyber attacks on the power grid combine physical disruptions with compromised data to destabilize cyber-physical systems. We demonstrate that data denial attacks, where adversaries block measurements in a targeted region while triggering a line outage, reduce detection performance by more than 86\%, rendering standard data-driven methods ineffective. We propose AdaptoNet, a modular neural network that adapts to measurement availability through conditional controls. AdaptoNet pairs a frozen foundational module trained on complete data with a trainable adaptive module, conditioned on a binary measurement-availability vector, enabling the model to distinguish between denied and anomalous data without retraining the foundational module. Evaluated across four IEEE test systems (30-, 39-, 57-, and 118-bus) under in-region attacks blocking up to 20% of measurements, AdaptoNet recovers F1 from below 12\% to above 81\%, an approximate sevenfold improvement approaching the 89%-99% baseline with complete measurements.
