A survey of machine learning techniques for detecting anomaly in internet of things (IoT) | [Journal of Independent Studies and Research Computing • 2023]

Author(s):

1. Imran: Malaysian Institute of, Information Technology,Universiti Kuala, Lumpur, Kuala Lumpur,Malaysia

2. Syed Mubashir Ali: College of Computing and, Information Sciences, Karachi, Institute of Economics and, Technology KIET,Karachi,Pakistan

3. Rizwan Faiz: Faculty of Computing, Riphah International, University,(RIU), Islamabad,Pakistan

4. Muhammad Mansoor Alam: Faculty of Computing and Informatics, Multimedia University (MMU) Cyberjaya , Malaysia

5. Syed Kashif Ali Quadri: Department of Computer Science / Artificial Intelligence, Sindh Madressatul Islam University, Karachi, Sindh, Pakistan

6. Muhammad Razeen Bari: Department of Artificial, Intelligence, Sindh Madressatul, Islam University,Karachi,Pakistan

7. Muhammad Farooq Shibli: Department of Computer Science, DHA Suffa University,(DSU), Karachi,Pakistan

Abstract:

In recent years, there has been a lot of focus on anomaly detection. Technological advancements, such as the Internet of Things (IoT), are rapidly being acknowledged as critical means for data streams that create massive amounts of data in real time from a variety of applications. Analyzing this gathered data to detect abnormal occurrences helps decrease functional hazards and avoid unnoticed errors that cause programme delay. Methods for evaluating specific anomalous behaviors in IoT data stream sources have been established and developed in the current literature. Unfortunately, there are very few thorough researches that include all elements of IoT data acquisition. As a result, this article seeks to address this void by presenting a comprehensive picture of numerous cutting-edge solutions on the fundamental concerns and essential issues in IoT data. The data type, types of anomalies, the learning method, datasets, and evaluation criteria are all described. Lastly, the issues that necessitate further investigation and future approaches are highlighted.

Page(s): 40-51

DOI: 10.31645/JISRC.23.21.1.5

Published: Journal: Journal of Independent Studies and Research Computing, Volume: 21, Issue: 1, Year: 2023

Keywords:

Machine learning , Internet of Things , Data Stream

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