Using Machine Learning for Security Issues in Cognitive IoT

Conference paper

Alzuabi, W., Elmedany, W. and Sharif, S. 2023. Using Machine Learning for Security Issues in Cognitive IoT. SCS-2023: 7th IET Smart Cities Symposium. Manama, Bahrain 03 - 05 Dec 2023 Institution of Engineering and Technology.
AuthorsAlzuabi, W., Elmedany, W. and Sharif, S.
TypeConference paper
Abstract

Cognitive learning is progressively prospering in
the field of Internet of Things (IoT). With the advancement
in IoT, data generation rate has also increased, whereas issues like performance, attacks on the data, security of the data, and inadequate data resources are yet to be resolved. Recent studies are mostly focusing on the security of the data which can be handled by machine learning. Security and privacy of devices intrusion detection their success in achieving classification accuracy, machine deep learning with intrusion detection systems have greatly increased popularity. However, the need to store communication centralized server compromise privacy and security. Contrast, Federated Learning (FL) fits appropriately as a privacy-preserving decentralized learning technique that trains locally transfer the parameters the centralized instead of purpose current research provide thorough and application FL intrusion detection systems. Machine Learning (ML) and Deep Learning (DL) approaches, which may embed intelligence in IoT devices and networks, can help to overcome a variety of security challenges. The research includes a detailed overview of the application of FL in several anomaly detection domains. In addition, it increases understanding of ML and its application to the field of the Cognitive Internet of Things (CIoT). This endeavour also includes something crucial . The relevant FL implementation issues are also noted, revealing potential areas for further research. The researcher emphasised the flaws in current security remedies, which call for ML and DL methods. The report goes into great detail on how ML and DL are now being utilised to help handle various security issues that IoT networks are facing. Random Neural Networks that have been trained using data retrieved by Cognitive Packets make the routing decisions. A number of potential future directions for ML and DL-based IoT security research are also included in the study. The report concludes by outlining workable responses to the problem. The paper closes by offering a beginning point for future study, describing workable answers to the problem of FL-based intrusion detection system implementation.

Year2023
ConferenceSCS-2023: 7th IET Smart Cities Symposium
PublisherInstitution of Engineering and Technology
Accepted author manuscript
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Publication process dates
Accepted11 Nov 2023
Deposited15 Nov 2023
JournalIET Conference Proceedings
Journal citationIn Press
ISSN2732-4494
Book titleProceedings Volume of the 7th IET Smart Cities Symposium
Copyright holder© 2023, Institution of Engineering and Technology
Additional information

This paper is a postprint of a paper submitted to and accepted for publication in [SCS-2023] and is subject to Institution of Engineering and Technology Copyright. The copy of record will be available at the IET Digital Library

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