Anomaly Detection via Controlled Sensing and Deep Active Inference
- URL: http://arxiv.org/abs/2105.06288v1
- Date: Wed, 12 May 2021 17:54:02 GMT
- Title: Anomaly Detection via Controlled Sensing and Deep Active Inference
- Authors: Geethu Joseph, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar, Pramod
K. Varshney
- Abstract summary: In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes.
We develop a sequential selection algorithm that decides which processes to be probed at every instant to detect the anomalies.
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.
- Score: 43.07302992747749
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Anomaly Detection via Learning-Based Sequential Controlled Sensing [25.282033825977827]
We address the problem of detecting anomalies among a set of binary processes via learning-based controlled sensing.
To identify the anomalies, the decision-making agent is allowed to observe a subset of the processes at each time instant.
Our objective is to design a sequential selection policy that dynamically determines which processes to observe at each time.
arXiv Detail & Related papers (2023-11-30T07:49:33Z) - Temporal Detection of Anomalies via Actor-Critic Based Controlled
Sensing [31.841289319809807]
We address the problem of monitoring a set of binary processes and generating an alert when the number of anomalies among them exceeds a threshold.
For this, the decision-maker selects and probes a subset of the processes to obtain noisy estimates of their states.
Using the posterior probability, we construct a Markov decision process and solve it using deep actor-critic reinforcement learning.
arXiv Detail & Related papers (2022-01-03T20:59:40Z) - Scalable and Decentralized Algorithms for Anomaly Detection via
Learning-Based Controlled Sensing [40.14838268469627]
We develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant.
The objective of the detection algorithm is to identify the anomalies with an accuracy exceeding the desired value.
arXiv Detail & Related papers (2021-12-08T11:20:36Z) - Machine Learning for Online Algorithm Selection under Censored Feedback [71.6879432974126]
In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms.
For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime.
In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem.
We adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon.
arXiv Detail & Related papers (2021-09-13T18:10:52Z) - A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled
Sensing [37.78306297797]
We develop an anomaly detection algorithm that chooses the process to be observed at a given time instant.
The objective of the detection algorithm is to arrive at a decision with an accuracy exceeding a desired value.
Unlike prior work on this topic that has exponential complexity in the number of processes, our algorithm has computational and memory requirements that are both in the number of processes.
arXiv Detail & Related papers (2021-05-12T17:46:01Z) - Learned Block Iterative Shrinkage Thresholding Algorithm for
Photothermal Super Resolution Imaging [52.42007686600479]
We propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network.
We show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters.
arXiv Detail & Related papers (2020-12-07T09:27:16Z) - Run2Survive: A Decision-theoretic Approach to Algorithm Selection based
on Survival Analysis [75.64261155172856]
survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime.
We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive.
In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
arXiv Detail & Related papers (2020-07-06T15:20:17Z) - Anomaly Detection Under Controlled Sensing Using Actor-Critic
Reinforcement Learning [31.841289319809807]
We consider the problem of detecting anomalies among a given set of processes using 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.
We propose a sequential sensor selection policy that dynamically determines which processes to observe at each time and when to terminate the detection algorithm.
arXiv Detail & Related papers (2020-05-26T22:53:17Z) - Active Model Estimation in Markov Decision Processes [108.46146218973189]
We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision process (MDP)
We show that our Markov-based algorithm outperforms both our original algorithm and the maximum entropy algorithm in the small sample regime.
arXiv Detail & Related papers (2020-03-06T16:17:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.