Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based method for sequential anomaly detection in multiple processes, optimizing sensor selection to balance detection speed and sensing costs without prior knowledge of the number of anomalies.

Contribution

It formulates the anomaly detection as a Markov decision process and applies actor-critic reinforcement learning to develop an adaptive, low-complexity sensor selection policy.

Findings

Effective detection with minimal sensing cost

Adaptability to unknown process dependence patterns

Good detection accuracy demonstrated in experiments

Abstract

We consider the problem of detecting anomalies among a given set of processes using their noisy binary sensor measurements. The noiseless sensor measurement corresponding to a normal process is 0, and the measurement is 1 if the process is anomalous. The decision-making algorithm is assumed to have no knowledge of the number of anomalous processes. The algorithm is allowed to choose a subset of the sensors at each time instant until the confidence level on the decision exceeds the desired value. Our objective is to design a sequential sensor selection policy that dynamically determines which processes to observe at each time and when to terminate the detection algorithm. The selection policy is designed such that the anomalous processes are detected with the desired confidence level while incurring minimum cost which comprises the delay in detection and the cost of sensing. We cast this problem as a sequential hypothesis testing problem within the framework of Markov decision processes, and solve it using the actor-critic deep reinforcement learning algorithm. This deep neural network-based algorithm offers a low complexity solution with good detection accuracy. We also study the effect of statistical dependence between the processes on the algorithm performance. Through numerical experiments, we show that our algorithm is able to adapt to any unknown statistical dependence pattern of the processes.