Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing

Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing

Joseph, G. (TU Delft Signal Processing Systems)
Zhong, Chen (Syracuse University)
Gursoy, M. Cenk (Syracuse University)
Velipasalar, Senem (Syracuse University)
Varshney, Pramod (Syracuse University)

2023

We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes a subset of the processes at any given time instant and obtains a noisy binary indicator of whether or not the corresponding process is anomalous. We develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant, decides when to stop taking observations, and declares the decision on anomalous processes. The objective of the detection algorithm is to identify the anomalies with an accuracy exceeding the desired value while minimizing the delay in decision making. We devise a centralized algorithm where the processes are jointly selected by a common agent as well as a decentralized algorithm where the decision of whether to select a process is made independently for each process. Our algorithms rely on a Markov decision process defined using the marginal probability of each process being normal or anomalous, conditioned on the observations. We implement the detection algorithms using the deep actor-critic reinforcement learning framework. Unlike prior work on this topic that has exponential complexity in the number of processes, our algorithms have computational and memory requirements that are both polynomial in the number of processes. We demonstrate the efficacy of these algorithms using numerical experiments by comparing them with state-of-the-art methods.

Active hypothesis testing
deep learning
reinforcement learning
actor-critic algorithm
quickest state estimation
sequential decision-making
sequential sensing

http://resolver.tudelft.nl/uuid:b46b1e45-2bbd-4ce5-8e70-f052d5a36737

https://doi.org/10.1109/TSIPN.2023.3313818

2024-03-11

2373-776X

IEEE Transactions on Signal and Information Processing over Networks, 9, 640-654

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 G. Joseph, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar, Pramod Varshney