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.

翻译：随着物联网(IoT)装置和系统的数量激增,已经开发了IoT数据分析技术,以探测恶意网络攻击和确保IoT系统的安全;然而,概念漂移问题经常发生在IoT数据分析中,因为IoT数据往往是动态数据流,随着时间的推移而变化,造成模型退化和攻击探测失败,这是因为传统数据分析模型是无法适应数据分布变化的静态模型。在本文件中,我们提议了一个通过IoT数据流分析法漂移适应性IoT异常探测的“性能加权虚拟聚合(PWPAE)”框架。对两个公共数据集的实验表明,我们提议的PWPAE方法相对于最新方法而言是有效的。