Cover Image

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

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 reso...

Full description

Saved in:
Bibliographic Details
Main Authors: Shakirah, Saidin, Syifak Izhar, Hisham
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/40355/1/A%20survey%20on%20supervised%20machine%20learning%20in%20intrusion.pdf
http://umpir.ump.edu.my/id/eprint/40355/2/A%20survey%20on%20supervised%20machine%20learning%20in%20intrusion%20detection%20systems%20for%20Internet%20of%20Things_ABS.pdf
http://umpir.ump.edu.my/id/eprint/40355/
https://doi.org/10.1109/ICSECS58457.2023.10256275
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.40355
record_format eprints
spelling my.ump.umpir.403552024-04-16T04:13:42Z http://umpir.ump.edu.my/id/eprint/40355/ A survey on supervised machine learning in intrusion detection systems for Internet of Things Shakirah, Saidin Syifak Izhar, Hisham QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) 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. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40355/1/A%20survey%20on%20supervised%20machine%20learning%20in%20intrusion.pdf pdf en http://umpir.ump.edu.my/id/eprint/40355/2/A%20survey%20on%20supervised%20machine%20learning%20in%20intrusion%20detection%20systems%20for%20Internet%20of%20Things_ABS.pdf 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 https://doi.org/10.1109/ICSECS58457.2023.10256275
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Shakirah, Saidin
Syifak Izhar, Hisham
A survey on supervised machine learning in intrusion detection systems for Internet of Things
description 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.
format Conference or Workshop Item
author Shakirah, Saidin
Syifak Izhar, Hisham
author_facet Shakirah, Saidin
Syifak Izhar, Hisham
author_sort Shakirah, Saidin
title A survey on supervised machine learning in intrusion detection systems for Internet of Things
title_short A survey on supervised machine learning in intrusion detection systems for Internet of Things
title_full A survey on supervised machine learning in intrusion detection systems for Internet of Things
title_fullStr A survey on supervised machine learning in intrusion detection systems for Internet of Things
title_full_unstemmed A survey on supervised machine learning in intrusion detection systems for Internet of Things
title_sort survey on supervised machine learning in intrusion detection systems for internet of things
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40355/1/A%20survey%20on%20supervised%20machine%20learning%20in%20intrusion.pdf
http://umpir.ump.edu.my/id/eprint/40355/2/A%20survey%20on%20supervised%20machine%20learning%20in%20intrusion%20detection%20systems%20for%20Internet%20of%20Things_ABS.pdf
http://umpir.ump.edu.my/id/eprint/40355/
https://doi.org/10.1109/ICSECS58457.2023.10256275
_version_ 1822924223679561728
score 13.23243

Similar Items