A survey on supervised machine learning in intrusion detection systems for Internet of Things

Shakirah, Saidin and Syifak Izhar, Hisham (2023) A survey on supervised machine learning in intrusion detection systems for Internet of Things. In: 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023 , 25-27 August 2023 , Penang. pp. 419-423. (192961). ISBN 979-835031093-1

	Pdf A survey on supervised machine learning in intrusion.pdf Restricted to Repository staff only Download (1MB) | Request a copy
Preview	Pdf A survey on supervised machine learning in intrusion detection systems for Internet of Things_ABS.pdf Download (1MB) | Preview

Abstract

The Internet of Things (IoT) is expanding exponentially, increasing network traffic flow. This trend causes network security vulnerabilities and draws the attention of cybercriminals. Consequently, an intrusion detection system is designed to identify various network attacks and provide network resource protection. On the other hand, building a steadfast intrusion detection system is difficult since there are numerous flaws to address, such as the presence of supernumerary and irrelevant features in the dataset, leading to low detection accuracy and a high false alarm rate. To address these flaws, researchers are attempting to research on applying supervised machine learning techniques in intrusion detection systems for IoT. Therefore, this paper explores the prevailing machine learning techniques utilized in the intrusion detection system research area to provide better insight in this field.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Internet of Things; Intrusion detection system; Security; Supervised machine learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	Institute of Postgraduate Studies Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	16 Apr 2024 04:13
Last Modified:	16 Apr 2024 04:13
URI:	http://umpir.ump.edu.my/id/eprint/40355
Download Statistic:	View Download Statistics

Actions (login required)

View Item