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

arXiv: https://arxiv.org/abs/2112.04912 · HTML: https://mlsanity.com/paper/2112.04912/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 8 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Policy Design for Active Sequential Hypothesis Testing using Deep Learning](https://mlsanity.com/paper/1810.04859/) (2018)
- [Deep Actor-Critic Reinforcement Learning for Anomaly Detection](https://mlsanity.com/paper/1908.10755/) (2019)
- [Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning](https://mlsanity.com/paper/2006.01044/) (2020)
- [A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing](https://mlsanity.com/paper/2105.06289/) (2021)
- [Active Anomaly Detection with Switching Cost](https://mlsanity.com/paper/1810.11800/) (2018)
- [Sequential Experiment Design for Hypothesis Verification](https://mlsanity.com/paper/1812.01137/) (2018)

## Built on by (most cited first, 4 of 4)

- [Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learning](https://mlsanity.com/paper/2303.10623/) (2023)
- [Anomaly Detection via Learning-Based Sequential Controlled Sensing](https://mlsanity.com/paper/2312.00088/) (2023)
- [Evasive Active Hypothesis Testing with Deep Neuroevolution: The Single- and Multi-Agent Cases](https://mlsanity.com/paper/2403.10112/) (2024)
- [Searching for a Hidden Markov Anomaly over Multiple Processes](https://mlsanity.com/paper/2506.17108/) (2025)

## Abstract (arXiv)

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. In this setting, 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.