mlsanityData to 7 Oct 2026

A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams

arXiv 2104.10529 ↗First posted 21 Apr 2021cs.LG cs.AIcited by 28

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Abstract (arXiv)

In recent years, with the increasing popularity of "Smart Technology", the number of Internet of Things (IoT) devices and systems have surged significantly. Various IoT services and functionalities are based on the analytics of IoT streaming data. However, IoT data analytics faces concept drift challenges due to the dynamic nature of IoT systems and the ever-changing patterns of IoT data streams. In this article, we propose an adaptive IoT streaming data analytics framework for anomaly detection use cases based on optimized LightGBM and concept drift adaptation. A novel drift adaptation method named Optimized Adaptive and Sliding Windowing (OASW) is proposed to adapt to the pattern changes of online IoT data streams. Experiments on two public datasets show the high accuracy and efficiency of our proposed adaptive LightGBM model compared against other state-of-the-art approaches. The proposed adaptive LightGBM model can perform continuous learning and drift adaptation on IoT data streams without human intervention.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 21 Apr 2021.

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6 of 19 references.

References · 6

  1. 2020Learning under Concept Drift: A Reviewcited by 470
  2. 2020On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practicecited by 181
  3. 2018Scikit-Multiflow: A Multi-output Streaming Frameworkcited by 70
  4. 2018Extremely Fast Decision Treecited by 42
  5. 2019Tree-based Intelligent Intrusion Detection System in Internet of Vehiclescited by 25
  6. 2020Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approachcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 9; 2023: 3; 2024: 1; 2025: 7; 2026: 2.

28 papers.

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  1. 2021MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehiclescited by 36
  2. 2021PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streamscited by 17
  3. 2022IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspectivecited by 15
  4. 2022A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehiclescited by 11
  5. 2021Concept Drift Detection in Federated Networked Systemscited by 10
  6. 2021Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detectioncited by 9
  7. 2022Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Driftcited by 9
  8. 2022LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehiclescited by 6
  9. 2025Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysiscited by 4
  10. 2023Fast Context Adaptation in Cost-Aware Continual Learningcited by 2
  11. 2025A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Datacited by 2
  12. 2025Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networkscited by 2
16 more
  1. 2025RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budgetcited by 2
  2. 2022A Model Drift Detection and Adaptation Framework for 5G Core Networkscited by 1
  3. 2022Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptationcited by 1
  4. 2024An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attackscited by 1
  5. 2025Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection Systemcited by 1
  6. 2021Assessing Machine Learning Approaches to Address IoT Sensor Driftcited by 0
  7. 2021An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Datacited by 0
  8. 2022Anomaly Detection in Intra-Vehicle Networkscited by 0
  9. 2022An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analyticscited by 0
  10. 2022A Sequential Concept Drift Detection Method for On-Device Learning on Low-End Edge Devicescited by 0
  11. 2023Modular Simulation Environment Towards OTN AI-based Solutionscited by 0
  12. 2023Forensic Data Analytics for Anomaly Detection in Evolving Networkscited by 0
  13. 2025Data-Driven Heat Pump Management: Combining Machine Learning with Anomaly Detection for Residential Hot Water Systemscited by 0
  14. 2025Uncertainty Guided Online Ensemble for Non-stationary Data Streams in Fusion Sciencecited by 0
  15. 2026A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturingcited by 0
  16. 2026Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environmentscited by 0