Deep Actor-Critic Reinforcement Learning for Anomaly Detection
This work addresses anomaly detection for applications like smart homes and IoT, but it appears incremental as it builds on existing reinforcement learning methods for a specific sequential testing scenario.
The paper tackles the problem of anomaly detection in systems with multiple sensors by proposing a deep actor-critic reinforcement learning framework to dynamically select sensors, aiming to maximize decision confidence and minimize stopping time. Simulation results show comparisons with the Chernoff test in terms of claim delay and loss.
Anomaly detection is widely applied in a variety of domains, involving for instance, smart home systems, network traffic monitoring, IoT applications and sensor networks. In this paper, we study deep reinforcement learning based active sequential testing for anomaly detection. We assume that there is an unknown number of abnormal processes at a time and the agent can only check with one sensor in each sampling step. To maximize the confidence level of the decision and minimize the stopping time concurrently, we propose a deep actor-critic reinforcement learning framework that can dynamically select the sensor based on the posterior probabilities. We provide simulation results for both the training phase and testing phase, and compare the proposed framework with the Chernoff test in terms of claim delay and loss.