An Efficient Spam Detection Technique for IoT Devices Using Machine Learning

Journal article


Makkar, A., Garg, S., Kumar, N., Hossain, M.S., Ghoneim, A. and Alrashoud, M. 2021. An Efficient Spam Detection Technique for IoT Devices Using Machine Learning. IEEE Transactions on Industrial Informatics. Vol 17 (Issue 2), pp. 903-912. https://doi.org/10.1109/tii.2020.2968927
AuthorsMakkar, A., Garg, S., Kumar, N., Hossain, M.S., Ghoneim, A. and Alrashoud, M.
Abstract

The Internet of Things (IoT) is a group of millions of devices having sensors and actuators linked over wired or wireless channel for data transmission. IoT has grown rapidly over the past decade with more than 25 billion devices expected to be connected by 2020. The volume of data released from these devices will increase many-fold in the years to come. In addition to an increased volume, the IoT devices produces a large amount of data with a number of different modalities having varying data quality defined by its speed in terms of time and position dependency. In such an environment, machine learning (ML) algorithms can play an important role in ensuring security and authorization based on biotechnology, anomalous detection to improve the usability, and security of IoT systems. On the other hand, attackers often view learning algorithms to exploit the vulnerabilities in smart IoT-based systems. Motivated from these, in this article, we propose the security of the IoT devices by detecting spam using ML. To achieve this objective, Spam Detection in IoT using Machine Learning framework is proposed. In this framework, five ML models are evaluated using various metrics with a large collection of inputs features sets. Each model computes a spam score by considering the refined input features. This score depicts the trustworthiness of IoT device under various parameters. REFIT Smart Home data set is used for the validation of proposed technique. The results obtained proves the effectiveness of the proposed scheme in comparison to the other existing schemes.

KeywordsCommunication system security; Internet of Things (IoT); machine learning
Year2021
JournalIEEE Transactions on Industrial Informatics
Journal citationVol 17 (Issue 2), pp. 903-912
PublisherIEEE
ISSN1941-0050
Digital Object Identifier (DOI)https://doi.org/10.1109/tii.2020.2968927
Web address (URL)http://www.scopus.com/inward/record.url?eid=2-s2.0-85097341478&partnerID=MN8TOARS
Output statusPublished
Publication dates
Online23 Jan 2020
Feb 2021
Publication process dates
Deposited22 May 2023
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