Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Continual LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks
Artificial neural networks are well-known to be susceptible to catastrophic forgetting when continually learning from sequences of tasks. Various continual (or "incremental") learning approaches have been proposed to avo…
Backdoor AttackContinual LearningIncremental LearningMisinformationData Augmentation for Continual RL via Adversarial Gradient Episodic Memory
Data efficiency of learning, which plays a key role in the Reinforcement Learning (RL) training process, becomes even more important in continual RL with sequential environments. In continual RL, the learner interacts wi…
BenchmarkingData AugmentationReinforcement Learning (RL)Parseval Regularization for Continual Reinforcement Learning
Loss of plasticity, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequences of tasks -- all referring to the increased difficulty in training on new task…
reinforcement-learningReinforcement LearningContinual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction
Deep reinforcement learning has made significant progress in the field of continuous control, such as physical control and autonomous driving. However, it is challenging for a reinforcement model to learn a policy for ea…
Autonomous Drivingcontinuous-controlContinuous ControlDeep Reinforcement Learning+4Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift
Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security consisting in the detection of hostile action based on the unusual nature of events obse…
Active LearningAnomaly Detection