Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data

• URL: http://arxiv.org/abs/2208.01841v1
• Date: Wed, 3 Aug 2022 04:52:08 GMT
• Title: Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data
• Authors: Wenkai Li, Cheng Feng, Ting Chen, Jun Zhu
• Abstract summary: Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era. Deep TSAD methods typically rely on a clean training dataset that is not polluted by anomalies to learn the "normal profile" of the underlying dynamics. We propose a model-agnostic method which can effectively improve the robustness of learning mainstream deep TSAD models with potentially contaminated data.
• Score: 29.808942473293108
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era. In recent years, a large number of deep neural network-based methods have been proposed, demonstrating significantly better performance than conventional methods on addressing challenging TSAD problems in a variety of areas. Nevertheless, these deep TSAD methods typically rely on a clean training dataset that is not polluted by anomalies to learn the "normal profile" of the underlying dynamics. This requirement is nontrivial since a clean dataset can hardly be provided in practice. Moreover, without the awareness of their robustness, blindly applying deep TSAD methods with potentially contaminated training data can possibly incur significant performance degradation in the detection phase. In this work, to tackle this important challenge, we firstly investigate the robustness of commonly used deep TSAD methods with contaminated training data which provides a guideline for applying these methods when the provided training data are not guaranteed to be anomaly-free. Furthermore, we propose a model-agnostic method which can effectively improve the robustness of learning mainstream deep TSAD models with potentially contaminated data. Experiment results show that our method can consistently prevent or mitigate performance degradation of mainstream deep TSAD models on widely used benchmark datasets.

Related papers

• Reliable Deep Diffusion Tensor Estimation: Rethinking the Power of Data-Driven Optimization Routine [17.516054970588137]
  This work introduces a data-driven optimization-based method termed DoDTI. The proposed method attains state-of-the-art performance in DTI parameter estimation. Notably, it demonstrates superior generalization, accuracy, and efficiency, rendering it highly reliable for widespread application in the field.
  arXiv  Detail & Related papers  (2024-09-04T07:35:12Z)
• Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation [0.0]
  Deep learning (DL) has emerged as a crucial tool in network anomaly detection (NAD) for cybersecurity. While DL models for anomaly detection excel at extracting features and learning patterns from data, they are vulnerable to data contamination. This study evaluates the robustness of six unsupervised DL algorithms against data contamination.
  arXiv  Detail & Related papers  (2024-07-11T19:47:37Z)
• 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)
• Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation [10.41066461952124]
  The paper proposes an autoencoder-based incremental learning method with drift detection (strAEm++DD) Our proposed method strAEm++DD leverages on the advantages of both incremental learning and drift detection. We conduct an experimental study using real-world and synthetic datasets with severe or extreme class imbalance, and provide an empirical analysis of strAEm++DD.
  arXiv  Detail & Related papers  (2023-05-15T19:40:04Z)
• Efficient Deep Reinforcement Learning Requires Regulating Overfitting [91.88004732618381]
  We show that high temporal-difference (TD) error on the validation set of transitions is the main culprit that severely affects the performance of deep RL algorithms. We show that a simple online model selection method that targets the validation TD error is effective across state-based DMC and Gym tasks.
  arXiv  Detail & Related papers  (2023-04-20T17:11:05Z)
• Dataset Distillation: A Comprehensive Review [76.26276286545284]
  dataset distillation (DD) aims to derive a much smaller dataset containing synthetic samples, based on which the trained models yield performance comparable with those trained on the original dataset. This paper gives a comprehensive review and summary of recent advances in DD and its application.
  arXiv  Detail & Related papers  (2023-01-17T17:03:28Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Distributed Dynamic Safe Screening Algorithms for Sparse Regularization [73.85961005970222]
  We propose a new distributed dynamic safe screening (DDSS) method for sparsity regularized models and apply it on shared-memory and distributed-memory architecture respectively. We prove that the proposed method achieves the linear convergence rate with lower overall complexity and can eliminate almost all the inactive features in a finite number of iterations almost surely.
  arXiv  Detail & Related papers  (2022-04-23T02:45:55Z)
• TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data [13.864161788250856]
  TranAD is a deep transformer network based anomaly detection and diagnosis model. It uses attention-based sequence encoders to swiftly perform inference with the knowledge of the broader temporal trends in the data. TranAD can outperform state-of-the-art baseline methods in detection and diagnosis performance with data and time-efficient training.
  arXiv  Detail & Related papers  (2022-01-18T19:41:29Z)
• 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)

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.