Anomaly Detection via Controlled Sensing and Deep Active Inference

TL;DR

This paper introduces a deep active inference-based sequential algorithm for anomaly detection that efficiently identifies anomalous processes by minimizing measurements and delay, outperforming reinforcement learning methods.

Contribution

It develops a novel active inference framework with deep neural network approximation for anomaly detection, improving efficiency and accuracy over existing methods.

Findings

Outperforms deep actor-critic reinforcement learning methods

Reduces measurement count and detection delay

Achieves higher detection accuracy

Abstract

In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes. To this end, the decision-making agent probes a subset of processes at every time instant and obtains a potentially erroneous estimate of the binary variable which indicates whether or not the corresponding process is anomalous. The agent continues to probe the processes until it obtains a sufficient number of measurements to reliably identify the anomalous processes. In this context, we develop a sequential selection algorithm that decides which processes to be probed at every instant to detect the anomalies with an accuracy exceeding a desired value while minimizing the delay in making the decision and the total number of measurements taken. Our algorithm is based on active inference which is a general framework to make sequential decisions in order to maximize the notion of free energy. We define the free energy using the objectives of the selection policy and implement the active inference framework using a deep neural network approximation. Using numerical experiments, we compare our algorithm with the state-of-the-art method based on deep actor-critic reinforcement learning and demonstrate the superior performance of our algorithm.