  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.
