Deep Actor-Critic Reinforcement Learning for Anomaly Detection

TL;DR

This paper introduces a deep actor-critic reinforcement learning framework for anomaly detection that dynamically selects sensors to improve decision confidence and reduce detection time in various domains.

Contribution

It proposes a novel deep RL approach for active sequential testing in anomaly detection, outperforming traditional Chernoff tests in key metrics.

Findings

The framework effectively reduces claim delay.

It achieves higher confidence levels in detection.

Outperforms Chernoff test in simulations.

Abstract

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.