虚假数据注入检测的持续对抗强化学习 (CARL)：遗忘与可解释性
Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability
AI总结：
针对智能逆变器虚假数据注入攻击 (FDIA) 检测易受对抗样本攻击的问题，提出持续对抗强化学习 (CARL) 方法，将对抗样本纳入训练以提升检测可解释性，并采用联合训练策略解决灾难性遗忘问题。
摘要：
智能逆变器上的虚假数据注入攻击 (FDIA) 是与日益增长的可再生能源生产相关的一个日益严重的问题。虽然基于数据的 FDIA 检测方法也在积极开发中，但我们表明，它们仍然容易受到可以使用强化学习 (RL) 制造的具有影响力和隐蔽性的对抗样本的攻击。我们提出通过持续对抗强化学习 (CARL) 方法将此类对抗样本包含在基于数据的检测训练过程中。这样，可以精准定位基于数据的检测的缺陷，从而在其增量改进期间提供可解释性。我们表明，持续学习的实现容易受到灾难性遗忘的影响，并进一步表明可以通过对所有生成的 FDIA 场景采用联合训练策略来解决遗忘问题。
英文摘要：
False data injection attacks (FDIAs) on smart inverters are a growing concern linked to increased renewable energy production. While data-based FDIA detection methods are also actively developed, we show that they remain vulnerable to impactful and stealthy adversarial examples that can be crafted using Reinforcement Learning (RL). We propose to include such adversarial examples in data-based detection training procedure via a continual adversarial RL (CARL) approach. This way, one can pinpoint the deficiencies of data-based detection, thereby offering explainability during their incremental improvement. We show that a continual learning implementation is subject to catastrophic forgetting, and additionally show that forgetting can be addressed by employing a joint training strategy on all generated FDIA scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
