A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data

• URL: http://arxiv.org/abs/2501.15365v1
• Date: Sun, 26 Jan 2025 02:03:49 GMT
• Title: A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data
• Authors: Mahshid Rezakhani, Tolunay Seyfi, Fatemeh Afghah,
• Abstract summary: We propose a transfer learning-based model for anomaly detection in time-series datasets. Unlike conventional methods, our approach does not require labeled data in either the source or target domains. Empirical evaluations on novel intrusion detection datasets demonstrate that our model outperforms existing techniques.
• Score: 6.229535970620059
• License:
• Abstract: In recent years, rapid technological advancements and expanded Internet access have led to a significant rise in anomalies within network traffic and time-series data. Prompt detection of these irregularities is crucial for ensuring service quality, preventing financial losses, and maintaining robust security standards. While machine learning algorithms have shown promise in achieving high accuracy for anomaly detection, their performance is often constrained by the specific conditions of their training data. A persistent challenge in this domain is the scarcity of labeled data for anomaly detection in time-series datasets. This limitation hampers the training efficacy of both traditional machine learning and advanced deep learning models. To address this, unsupervised transfer learning emerges as a viable solution, leveraging unlabeled data from a source domain to identify anomalies in an unlabeled target domain. However, many existing approaches still depend on a small amount of labeled data from the target domain. To overcome these constraints, we propose a transfer learning-based model for anomaly detection in multivariate time-series datasets. Unlike conventional methods, our approach does not require labeled data in either the source or target domains. Empirical evaluations on novel intrusion detection datasets demonstrate that our model outperforms existing techniques in accurately identifying anomalies within an entirely unlabeled target domain.

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)
• Weakly Supervised Anomaly Detection via Knowledge-Data Alignment [24.125871437370357]
  Anomaly detection plays a pivotal role in numerous web-based applications, including malware detection, anti-money laundering, device failure detection, and network fault analysis. Weakly Supervised Anomaly Detection (WSAD) has been introduced with a limited number of labeled anomaly samples to enhance model performance. We introduce a novel framework Knowledge-Data Alignment (KDAlign) to integrate rule knowledge, typically summarized by human experts, to supplement the limited labeled data.
  arXiv  Detail & Related papers  (2024-02-06T07:57:13Z)
• 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)
• WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining [0.6754597324022876]
  Outlier detection can reveal vital information about system faults, fraudulent activities, and patterns in the data. This article proposed the WePaMaDM-Outlier Detection with distinct mass data mining domain. It also investigates the significance of data modeling in outlier detection techniques in surveillance, fault detection, and trend analysis.
  arXiv  Detail & Related papers  (2023-06-09T07:00:00Z)
• 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)
• 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)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• Unsupervised Domain Adaption of Object Detectors: A Survey [87.08473838767235]
  Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications. Learning highly accurate models relies on the availability of datasets with a large number of annotated images. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images.
  arXiv  Detail & Related papers  (2021-05-27T23:34:06Z)
• Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotator [2.8672054847109134]
  In some target problem domains, there are not many data samples available, which could hinder the learning process. We propose a weak adaptation learning (WAL) approach that leverages unlabeled data from a similar source domain. Our experiments demonstrate the effectiveness of our approach in learning an accurate classifier with limited labeled data in the target domain.
  arXiv  Detail & Related papers  (2021-02-15T06:19:25Z)
• TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition [77.77786072373942]
  This paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. We design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.
  arXiv  Detail & Related papers  (2021-02-09T03:07:07Z)
• 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.