Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2201.10323v1
• Date: Tue, 25 Jan 2022 13:54:19 GMT
• Title: Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection
• Authors: Hamza Bodor, Thai V. Hoang, Zonghua Zhang
• Abstract summary: A large set of anomaly detection algorithms have been deployed for detecting unexpected network incidents. Unsupervised anomaly detection algorithms often suffer from excessive false alarms. We propose to use active learning to introduce and benefit from the feedback of operators.
• Score: 2.1684857243537334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Key Performance Indicators (KPI), which are essentially time series data, have been widely used to indicate the performance of telecom networks. Based on the given KPIs, a large set of anomaly detection algorithms have been deployed for detecting the unexpected network incidents. Generally, unsupervised anomaly detection algorithms gain more popularity than the supervised ones, due to the fact that labeling KPIs is extremely time- and resource-consuming, and error-prone. However, those unsupervised anomaly detection algorithms often suffer from excessive false alarms, especially in the presence of concept drifts resulting from network re-configurations or maintenance. To tackle this challenge and improve the overall performance of unsupervised anomaly detection algorithms, we propose to use active learning to introduce and benefit from the feedback of operators, who can verify the alarms (both false and true ones) and label the corresponding KPIs with reasonable effort. Specifically, we develop three query strategies to select the most informative and representative samples to label. We also develop an efficient method to update the weights of Isolation Forest and optimally adjust the decision threshold, so as to eventually improve the performance of detection model. The experiments with one public dataset and one proprietary dataset demonstrate that our active learning empowered anomaly detection pipeline could achieve performance gain, in terms of F1-score, more than 50% over the baseline algorithm. It also outperforms the existing active learning based methods by approximately 6%-10%, with significantly reduced budget (the ratio of samples to be labeled).

Related papers

• A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks [6.537257913467249]
  We present a highly compact run-time monitoring approach for deep computer vision networks. It can efficiently detect silent data corruption originating from both hardware memory and input faults.
  arXiv  Detail & Related papers  (2023-10-31T10:45:55Z)
• PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection [65.24854366973794]
  Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in domains such as medicine, social networks, and e-commerce. We introduce a simple method termed PREprocessing and Matching (PREM for short) to improve the efficiency of GAD. Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities.
  arXiv  Detail & Related papers  (2023-10-18T02:59:57Z)
• Adaptive Thresholding Heuristic for KPI Anomaly Detection [1.57731592348751]
  A plethora of outlier detectors have been explored in the time series domain, however, in a business sense, not all outliers are anomalies of interest. This article proposes an Adaptive Thresholding Heuristic (ATH) to dynamically adjust the detection threshold based on the local properties of the data distribution and adapt to changes in time series patterns. Experimental results show that ATH is efficient making it scalable for near real time anomaly detection and flexible with forecasters and outlier detectors.
  arXiv  Detail & Related papers  (2023-08-21T06:45:28Z)
• 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)
• A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
  We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
  arXiv  Detail & Related papers  (2022-09-23T06:09:35Z)
• Anomaly Detection with Test Time Augmentation and Consistency Evaluation [13.709281244889691]
  We propose a simple, yet effective anomaly detection algorithm named Test Time Augmentation Anomaly Detection (TTA-AD) We observe that in-distribution data enjoy more consistent predictions for its original and augmented versions on a trained network than out-distribution data. Experiments on various high-resolution image benchmark datasets demonstrate that TTA-AD achieves comparable or better detection performance.
  arXiv  Detail & Related papers  (2022-06-06T04:27:06Z)
• Weakly Supervised Change Detection Using Guided Anisotropic Difusion [97.43170678509478]
  We propose original ideas that help us to leverage such datasets in the context of change detection. First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results. We then show its potential in two weakly-supervised learning strategies tailored for change detection.
  arXiv  Detail & Related papers  (2021-12-31T10:03:47Z)
• Anomaly Rule Detection in Sequence Data [2.3757190901941736]
  We present a new anomaly detection framework called DUOS that enables Discovery of Utility-aware Outlier Sequential rules from a set of sequences. In this work, we incorporate both the anomalousness and utility of a group, and then introduce the concept of utility-aware outlier rule (UOSR)
  arXiv  Detail & Related papers  (2021-11-29T23:52:31Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning [18.49217234413188]
  This paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles. We develop a novel batch active learning algorithm to improve the diversity of discovered anomalies. We present a data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the anomaly detector in a principled manner.
  arXiv  Detail & Related papers  (2019-01-23T23:41:11Z)

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.