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

arXiv: https://arxiv.org/abs/2104.10529 · HTML: https://mlsanity.com/paper/2104.10529/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Extremely Fast Decision Tree](https://mlsanity.com/paper/1802.08780/) (2018)
- [Tree-based Intelligent Intrusion Detection System in Internet of Vehicles](https://mlsanity.com/paper/1910.08635/) (2019)
- [Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach](https://mlsanity.com/paper/2012.11325/) (2020)

## Built on by (most cited first, 28 of 28)

- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams](https://mlsanity.com/paper/2109.05013/) (2021)
- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2201.11812/) (2022)
- [Concept Drift Detection in Federated Networked Systems](https://mlsanity.com/paper/2109.06088/) (2021)
- [Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Drift](https://mlsanity.com/paper/2211.12578/) (2022)
- [Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection](https://mlsanity.com/paper/2107.11514/) (2021)
- [LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles](https://mlsanity.com/paper/2208.03399/) (2022)
- [Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis](https://mlsanity.com/paper/2502.21286/) (2025)
- [Fast Context Adaptation in Cost-Aware Continual Learning](https://mlsanity.com/paper/2306.03887/) (2023)
- [A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data](https://mlsanity.com/paper/2501.15365/) (2025)
- [Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks](https://mlsanity.com/paper/2502.20627/) (2025)
- [RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget](https://mlsanity.com/paper/2505.24149/) (2025)
- [A Model Drift Detection and Adaptation Framework for 5G Core Networks](https://mlsanity.com/paper/2209.06852/) (2022)
- [Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation](https://mlsanity.com/paper/2211.01315/) (2022)
- [An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks](https://mlsanity.com/paper/2404.01109/) (2024)
- [Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection System](https://mlsanity.com/paper/2511.08491/) (2025)
- [Assessing Machine Learning Approaches to Address IoT Sensor Drift](https://mlsanity.com/paper/2109.04356/) (2021)
- [An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data](https://mlsanity.com/paper/2109.13828/) (2021)
- [Anomaly Detection in Intra-Vehicle Networks](https://mlsanity.com/paper/2205.03537/) (2022)
- [An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics](https://mlsanity.com/paper/2205.04622/) (2022)
- [A Sequential Concept Drift Detection Method for On-Device Learning on Low-End Edge Devices](https://mlsanity.com/paper/2212.09637/) (2022)
- [Modular Simulation Environment Towards OTN AI-based Solutions](https://mlsanity.com/paper/2306.11135/) (2023)
- [Forensic Data Analytics for Anomaly Detection in Evolving Networks](https://mlsanity.com/paper/2308.09171/) (2023)
- [Data-Driven Heat Pump Management: Combining Machine Learning with Anomaly Detection for Residential Hot Water Systems](https://mlsanity.com/paper/2506.15719/) (2025)
- [Uncertainty Guided Online Ensemble for Non-stationary Data Streams in Fusion Science](https://mlsanity.com/paper/2511.02092/) (2025)
- [A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing](https://mlsanity.com/paper/2607.18164/) (2026)
- [Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments](https://mlsanity.com/paper/2608.18555/) (2026)

## 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.