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

TL;DR

This paper introduces PWPAE, an ensemble framework designed to adapt to concept drift in IoT data streams, improving anomaly detection accuracy over static models.

Contribution

The paper proposes a novel ensemble method, PWPAE, specifically tailored for concept drift adaptation in IoT data stream analytics.

Findings

PWPAE outperforms existing methods in experiments.

Effective detection of IoT anomalies under concept drift.

Demonstrated robustness on public datasets.

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.