A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams

被引：66
作者：
Yang L. [1 ]
Shami A. [1 ]
机构：
[1] Western University, Ontario
来源：
IEEE Internet of Things Magazine | 2021年 / 4卷 / 02期
关键词：
Anomaly detection - Data Analytics;
D O I：
10.1109/IOTM.0001.2100012
中图分类号：
学科分类号：
摘要：
In recent years, with the increasing popularity of 'Smart Technology', the number of Internet of Things (IoT) devices and systems have surged significantly. Various IoT services and functionalities are based on the analytics of IoT streaming data. However, IoT data analytics faces concept drift challenges due to the dynamic nature of IoT systems and the ever-changing patterns of IoT data streams. In this article, we propose an adaptive IoT streaming data analytics framework for anomaly detection use cases based on optimized LightGBM and concept drift adaptation. A novel drift adaptation method named Optimized Adaptive and Sliding Windowing (OASW) is proposed to adapt to the pattern changes of online IoT data streams. Experiments on two public datasets show the high accuracy and efficiency of our proposed adaptive LightGBM model compared against other state-of-the-art approaches. The proposed adaptive LightGBM model can perform continuous learning and drift adaptation on IoT data streams without human intervention. © 2018 IEEE.
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收藏
页码：96 / 101
页数：5
相关论文
共 15 条
  • [1] Stack T., Iot Data continues to explode exponentially. who is using that data and how?, Cisco Blogs, (2020)
  • [2] Injadat M., Moubayed A., Shami A., Detecting botnet attacks in IoT environments: An optimized machine learning approach, IEEE-ICM2020, pp. 1-4, (2020)
  • [3] Yang L., Et al., Tree-based intelligent intrusion detection system in internet of vehicles, 2019 IEEE Glob. Commun. Conf., pp. 1-6, (2019)
  • [4] Cook A.A., Misirli G., Fan Z., Anomaly detection for IoT time-series data: A survey, IEEE Internet Things J., 7, 7, pp. 6481-6494, (2020)
  • [5] Ghani N.L.A., Aziz I.A., Mehat M., Concept drift detection on unlabeled data streams: A systematic literature review, 2020 IEEEConference on Big Data and Analytics (ICBDA), pp. 61-65, (2020)
  • [6] Lu J., Et al., Learning under concept drift: A review, IEEE Trans. Knowl. Data Eng., 31, 12, pp. 2346-2363, (2019)
  • [7] Ullah I., Mahmoud Q.H., A scheme for generating a dataset for anomalous activity detection in IoT networks, Advances in Artifi Cial Intelligence, pp. 508-520, (2020)
  • [8] Liu J., Kantarci B., Adams C., Machine learning-driven intrusion detection for contiki-NG-based IoT networks exposed to NSL-KDD dataset, WiseML 2020-Proc. 2nd ACM Work. Wirel. Secur. Mach. Learn., pp. 25-30, (2020)
  • [9] Losing V., Hammer B., Wersing H., KNN classifi er with self adjusting memory for heterogeneous concept drift, Proc. IEEE Int. Conf. Data Mining, ICDM, 1, pp. 291-300, (2017)
  • [10] Manapragada C., Webb G.I., Salehi M., Extremely fast decision tree, Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 1953-1962, (2018)
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