PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams
This addresses the problem of model degradation in IoT security analytics due to dynamic data streams, though it appears incremental as it builds on ensemble methods for drift adaptation.
The paper tackles concept drift in IoT data streams for anomaly detection by proposing the PWPAE ensemble framework, which adapts to data distribution changes to prevent model degradation and attack detection failure, with experiments on two public datasets showing its effectiveness compared to state-of-the-art methods.
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.