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

Abstract

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.

Accepted and to appear in IEEE GlobeCom 2021; Code is available at Github link: https://github.com/Western-OC2-Lab/PWPAE-Concept-Drift-Detection-and-Adaptation

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Cited by in corpus (8)

• A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles
• IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective
• A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems
• Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis
• Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks
• A Model Drift Detection and Adaptation Framework for 5G Core Networks
• An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics
• A Multi-Step Comparative Framework for Anomaly Detection in IoT Data Streams