ADT: Agent-based Dynamic Thresholding for Anomaly Detection

• URL: http://arxiv.org/abs/2312.01488v1
• Date: Sun, 3 Dec 2023 19:07:30 GMT
• Title: ADT: Agent-based Dynamic Thresholding for Anomaly Detection
• Authors: Xue Yang, Enda Howley, Micheal Schukat
• Abstract summary: We propose an agent-based dynamic thresholding (ADT) framework based on a deep Q-network. An auto-encoder is utilized in this study to obtain feature representations and produce anomaly scores for complex input data. ADT can adjust thresholds adaptively by utilizing the anomaly scores from the auto-encoder.
• Score: 4.356615197661274
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The complexity and scale of IT systems are increasing dramatically, posing many challenges to real-world anomaly detection. Deep learning anomaly detection has emerged, aiming at feature learning and anomaly scoring, which has gained tremendous success. However, little work has been done on the thresholding problem despite it being a critical factor for the effectiveness of anomaly detection. In this paper, we model thresholding in anomaly detection as a Markov Decision Process and propose an agent-based dynamic thresholding (ADT) framework based on a deep Q-network. The proposed method can be integrated into many systems that require dynamic thresholding. An auto-encoder is utilized in this study to obtain feature representations and produce anomaly scores for complex input data. ADT can adjust thresholds adaptively by utilizing the anomaly scores from the auto-encoder and significantly improve anomaly detection performance. The properties of ADT are studied through experiments on three real-world datasets and compared with benchmarks, hence demonstrating its thresholding capability, data-efficient learning, stability, and robustness. Our study validates the effectiveness of reinforcement learning in optimal thresholding control in anomaly detection.

Related papers

• Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation [37.72735288760648]
  We propose a learnable data augmentation-based time-series anomaly detection (LATAD) technique that is trained in a self-supervised manner. LATAD extracts discriminative features from time-series data through contrastive learning. As per the results, LATAD exhibited comparable or improved performance to the state-of-the-art anomaly detection assessments.
  arXiv  Detail & Related papers  (2024-06-18T04:25:56Z)
• Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution [89.16750999704969]
  Anomaly labels hinder traditional supervised models in time series anomaly detection. Various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. We propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD)
  arXiv  Detail & Related papers  (2023-11-19T05:37:18Z)
• Active anomaly detection based on deep one-class classification [9.904380236739398]
  We tackle two essential problems of active learning for Deep SVDD: query strategy and semi-supervised learning method. First, rather than solely identifying anomalies, our query strategy selects uncertain samples according to an adaptive boundary. Second, we apply noise contrastive estimation in training a one-class classification model to incorporate both labeled normal and abnormal data effectively.
  arXiv  Detail & Related papers  (2023-09-18T03:56:45Z)
• DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection [23.253175824487652]
  Self-supervised learning (SSL) has proven effective in solving various problems by generating internal supervisory signals. Unsupervised anomaly detection, which faces the high cost of obtaining true labels, is an area that can greatly benefit from SSL. We propose DSV (Discordance and Separability Validation), an unsupervised validation loss to select high-performing detection models with effective augmentation HPs.
  arXiv  Detail & Related papers  (2023-07-13T02:45:29Z)
• PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
  We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
  arXiv  Detail & Related papers  (2023-01-25T16:34:43Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• Robust Anomaly Detection for Time-series Data [8.206500786061406]
  This research combined the strengths of negative selection, unthresholded recurrence plots, and an extreme learning machine autoencoder. It proposed robust anomaly detection for time-series data (RADTD), which can automatically learn dynamical features in time series. Experiments showed that RADTD possessed higher accuracy and robustness than recurrence qualification analysis and extreme learning machine autoencoder.
  arXiv  Detail & Related papers  (2022-02-06T07:09:57Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Anomaly Detection Based on Selection and Weighting in Latent Space [73.01328671569759]
  We propose a novel selection-and-weighting-based anomaly detection framework called SWAD. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
  arXiv  Detail & Related papers  (2021-03-08T10:56:38Z)
• Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection [55.888835686183995]
  We propose a neural network-based meta-learning method for supervised anomaly detection. We experimentally demonstrate that the proposed method achieves better performance than existing anomaly detection and few-shot learning methods.
  arXiv  Detail & Related papers  (2021-03-01T01:43:04Z)
• Out-Of-Bag Anomaly Detection [0.9449650062296822]
  Data anomalies are ubiquitous in real world datasets, and can have an adverse impact on machine learning (ML) systems. We propose a novel model-based anomaly detection method, that we call Out-of-Bag anomaly detection. We show our method can improve the accuracy and reliability of an ML system as data pre-processing step via a case study on home valuation.
  arXiv  Detail & Related papers  (2020-09-20T06:01:52Z)

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.