Jahwar, Alan Fuad and Zeebaree, Subhi R. M. (2021) A State of the Art Survey of Machine Learning Algorithms for IoT Security. Asian Journal of Research in Computer Science, 9 (4). pp. 12-34. ISSN 2581-8260
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Abstract
The Internet of Things (IoT) is a paradigm shift that enables billions of devices to connect to the Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health, have created new challenges, chief among them security threats. To accommodate the current networking model, traditional security measures such as firewalls and Intrusion Detection Systems (IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one another, frequently used interchangeably when discussing technical services and collaborating to provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning (ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address various security issues. This paper systematically reviews the architecture of IoT applications, the security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally, we discuss the latest ML and DL strategies for solving various security issues in IoT networks.
Item Type: | Article |
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Subjects: | STM Archives > Computer Science |
Depositing User: | Unnamed user with email support@stmarchives.com |
Date Deposited: | 07 Feb 2023 12:17 |
Last Modified: | 29 Jul 2024 11:08 |
URI: | http://science.scholarsacademic.com/id/eprint/130 |