mlsanityData to 7 Oct 2026

PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams

arXiv 2109.05013 ↗First posted 10 Sep 2021cs.LG cs.AI cs.CRcited by 17

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Abstract (arXiv)

As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing model degradation and attack detection failure. This is because traditional data analytics models are static models that cannot adapt to data distribution changes. In this paper, we propose a Performance Weighted Probability Averaging Ensemble (PWPAE) framework for drift adaptive IoT anomaly detection through IoT data stream analytics. Experiments on two public datasets show the effectiveness of our proposed PWPAE method compared against state-of-the-art methods.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 12 of its 32 references that are in the map. First posted on arXiv 10 Sep 2021.

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12 of 32 references.

References · 12

  1. 2020Learning under Concept Drift: A Reviewcited by 470
  2. 2018Scikit-Multiflow: A Multi-output Streaming Frameworkcited by 70
  3. 2018Extremely Fast Decision Treecited by 42
  4. 2021MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehiclescited by 36
  5. 2021A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streamscited by 28
  6. 2019Tree-based Intelligent Intrusion Detection System in Internet of Vehiclescited by 25
  7. 2020Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detectioncited by 17
  8. 2021Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunitiescited by 14
  9. 2020Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networkscited by 13
  10. 2021Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systemscited by 10
  11. 2021Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detectioncited by 9
  12. 2020DNS Typo-squatting Domain Detection: A Data Analytics & Machine Learning Based Approachcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 9; 2023: 1; 2024: 0; 2025: 3; 2026: 3.

17 papers.

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  1. 2022A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehiclescited by 11
  2. 2021Concept Drift Detection in Federated Networked Systemscited by 10
  3. 2022Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Driftcited by 9
  4. 2022A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systemscited by 8
  5. 2022LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehiclescited by 6
  6. 2025Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysiscited by 4
  7. 2025Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networkscited by 2
  8. 2022A Model Drift Detection and Adaptation Framework for 5G Core Networkscited by 1
  9. 2022Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Frameworkcited by 1
  10. 2022LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devicescited by 1
  11. 2022Anomaly Detection in Intra-Vehicle Networkscited by 0
  12. 2022An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analyticscited by 0
5 more
  1. 2023Forensic Data Analytics for Anomaly Detection in Evolving Networkscited by 0
  2. 2025A Multi-Step Comparative Framework for Anomaly Detection in IoT Data Streamscited by 0
  3. 2026Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensemblingcited by 0
  4. 2026Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labelingcited by 0
  5. 2026An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networkscited by 0