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

arXiv: https://arxiv.org/abs/2109.05013 · HTML: https://mlsanity.com/paper/2109.05013/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (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)
- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams](https://mlsanity.com/paper/2104.10529/) (2021)
- [Tree-based Intelligent Intrusion Detection System in Internet of Vehicles](https://mlsanity.com/paper/1910.08635/) (2019)
- [Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection](https://mlsanity.com/paper/2008.03297/) (2020)
- [Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2101.03655/) (2021)
- [Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks](https://mlsanity.com/paper/2011.04121/) (2020)
- [Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems](https://mlsanity.com/paper/2102.10142/) (2021)
- [Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection](https://mlsanity.com/paper/2107.11514/) (2021)
- [DNS Typo-squatting Domain Detection: A Data Analytics & Machine Learning Based Approach](https://mlsanity.com/paper/2012.13604/) (2020)

## Built on by (most cited first, 17 of 17)

- [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)
- [A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems](https://mlsanity.com/paper/2210.01985/) (2022)
- [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)
- [Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks](https://mlsanity.com/paper/2502.20627/) (2025)
- [A Model Drift Detection and Adaptation Framework for 5G Core Networks](https://mlsanity.com/paper/2209.06852/) (2022)
- [Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework](https://mlsanity.com/paper/2211.01827/) (2022)
- [LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices](https://mlsanity.com/paper/2211.01840/) (2022)
- [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)
- [Forensic Data Analytics for Anomaly Detection in Evolving Networks](https://mlsanity.com/paper/2308.09171/) (2023)
- [A Multi-Step Comparative Framework for Anomaly Detection in IoT Data Streams](https://mlsanity.com/paper/2505.16872/) (2025)
- [Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling](https://mlsanity.com/paper/2602.12976/) (2026)
- [Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling](https://mlsanity.com/paper/2608.15465/) (2026)
- [An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2609.23097/) (2026)

## 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.