http://www.iaeme.com/IJARET/index.asp 2348 editor@iaeme.com International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 11, November 2020, pp. 2348-2362, Article ID: IJARET_11_11_233 Available online at http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=11 Journal Impact Factor (2020): 10.9475 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6480 and ISSN Online: 0976-6499 DOI: 10.34218/IJARET.11.11.2020.233 © IAEME Publication Scopus Indexed MACHINE LEARNING BASED OUTLIER DETECTION TECHNIQUES FOR IoT DATA ANALYSIS: A COMPREHENSIVE SURVEY Nenavath Chander Research Scholar, Department of Computer Science and Engineering, Osmania University, Hyderabad, Telangana, India. Dr. M. Upendra Kumar Professor, Department of Computer Science and Engineering, Muffakham Jah College of Engineering & Technology, Affiliated to OU, Hyderabad, Telangana, India. ABSTRACT These days, with the popularity and significant advancements of emerging technologies such as Internet of Things (IoT), Cyber-Physical-Systems (CPS), and other wireless sensor technologies, the huge volume of sensor data has generated for IoT devices is vast. In these data, identification and detection of outliers/anomalies is a challenging issue and raised as the primary importance of data analysis. In the olden days, the conventional outlier detection techniques are not effectively applied to deal with outliers over IoT data. Therefore, this paper explores a comprehensive survey of the latest Machine Learning (ML)-based outlier detection techniques for handling outliers in IoT data. Also, surveyed the various smart city based use cases related to IoT applications more significantly. Besides, the required performance evaluation metrics have been addressed for validating the results of ML-based outlier detection techniques. Finally, this article also addressed the possible open research issues that are necessary to deal with outliers in IoT sensor data. Key words: IoT, Machine Learning, Outlier detection, sensor data. Cite this Article: Nenavath Chander and M. Upendra Kumar, Machine Learning Based Outlier Detection Techniques for IoT Data Analysis: A Comprehensive Survey, International Journal of Advanced Research in Engineering and Technology, 11(11), 2020, pp. 2348-2362. http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=11 1. INTRODUCTION In the recent days, the era of big data and other sensor-based techniques, the huge volume of data being generated from various devices and communication protocols. The IoT is becoming a buzzword and making the real-world objects more meaningful and smart data objects. Over