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...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| 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 |
| _version_ | 1796996238608957440 |
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| author | Shakirah, Saidin Syifak Izhar, Hisham |
| author_facet | Shakirah, Saidin Syifak Izhar, Hisham |
| author_sort | Shakirah, Saidin |
| collection | UMP |
| 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. |
| first_indexed | 2024-04-22T01:25:59Z |
| format | Conference or Workshop Item |
| id | UMPir40355 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-04-22T01:25:59Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | UMPir403552024-04-16T04:13:42Z 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 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| 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 |
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