A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system
There are several limitations that facing intrusion-detection system in current days, such as high rates of false positive alerts, low detection rates of rare but dangerous attacks. Daily, there are reports of incidents such as major ex-filtration of data for the purposes of stealing identities. Hyb...
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2018
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/25403/ http://umpir.ump.edu.my/id/eprint/25403/ http://umpir.ump.edu.my/id/eprint/25403/1/A%20hybrid%20particle%20swarm%20optimization%20-%20extreme%20learning%20.pdf |
| id |
ump-25403 |
|---|---|
| recordtype |
eprints |
| spelling |
ump-254032019-11-12T08:39:31Z http://umpir.ump.edu.my/id/eprint/25403/ A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system M.H., Ali Mohamad, Fadlizolkipi Ahmad Firdaus, Zainal Abidin Nik Zulkarnaen, Khidzir QA Mathematics TK Electrical engineering. Electronics Nuclear engineering There are several limitations that facing intrusion-detection system in current days, such as high rates of false positive alerts, low detection rates of rare but dangerous attacks. Daily, there are reports of incidents such as major ex-filtration of data for the purposes of stealing identities. Hybrid model's approaches have been widely used to increase the effectiveness of intrusion-detection platforms. This work proposes the extreme learning machine (ELM) is one of the poplar machine learning algorithms which, easy to implement with excellent learning performance characteristics. However, the internal power parameters (weight and basis) of ELM are initialized at random, causing the algorithm to be unstable. The Particle swarm optimization (PSO) is a well-known meta-heuristic which is used in this research to optimize the ELM. Our propose model has been apple based as intrusion detection and validated based on NSL-KDD data set. Our developed model has been compared against a basic ELM. PSO-ELM has outperformed a basic model in the testing accuracy. IEEE 2018-05-09 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25403/1/A%20hybrid%20particle%20swarm%20optimization%20-%20extreme%20learning%20.pdf M.H., Ali and Mohamad, Fadlizolkipi and Ahmad Firdaus, Zainal Abidin and Nik Zulkarnaen, Khidzir (2018) A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system. In: 2018 IEEE 16th Student Conference on Research and Development, SCOReD 2018, 26 - 28 November 2018 , Selangor, Malaysia. pp. 1-4. (8711287). ISBN 978-153869175-5 https://doi.org/10.1109/SCORED.2018.8711287 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Malaysia Pahang |
| building |
UMP Institutional Repository |
| collection |
Online Access |
| language |
English |
| topic |
QA Mathematics TK Electrical engineering. Electronics Nuclear engineering |
| spellingShingle |
QA Mathematics TK Electrical engineering. Electronics Nuclear engineering M.H., Ali Mohamad, Fadlizolkipi Ahmad Firdaus, Zainal Abidin Nik Zulkarnaen, Khidzir A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system |
| description |
There are several limitations that facing intrusion-detection system in current days, such as high rates of false positive alerts, low detection rates of rare but dangerous attacks. Daily, there are reports of incidents such as major ex-filtration of data for the purposes of stealing identities. Hybrid model's approaches have been widely used to increase the effectiveness of intrusion-detection platforms. This work proposes the extreme learning machine (ELM) is one of the poplar machine learning algorithms which, easy to implement with excellent learning performance characteristics. However, the internal power parameters (weight and basis) of ELM are initialized at random, causing the algorithm to be unstable. The Particle swarm optimization (PSO) is a well-known meta-heuristic which is used in this research to optimize the ELM. Our propose model has been apple based as intrusion detection and validated based on NSL-KDD data set. Our developed model has been compared against a basic ELM. PSO-ELM has outperformed a basic model in the testing accuracy. |
| format |
Conference or Workshop Item |
| author |
M.H., Ali Mohamad, Fadlizolkipi Ahmad Firdaus, Zainal Abidin Nik Zulkarnaen, Khidzir |
| author_facet |
M.H., Ali Mohamad, Fadlizolkipi Ahmad Firdaus, Zainal Abidin Nik Zulkarnaen, Khidzir |
| author_sort |
M.H., Ali |
| title |
A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system |
| title_short |
A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system |
| title_full |
A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system |
| title_fullStr |
A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system |
| title_full_unstemmed |
A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system |
| title_sort |
hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system |
| publisher |
IEEE |
| publishDate |
2018 |
| url |
http://umpir.ump.edu.my/id/eprint/25403/ http://umpir.ump.edu.my/id/eprint/25403/ http://umpir.ump.edu.my/id/eprint/25403/1/A%20hybrid%20particle%20swarm%20optimization%20-%20extreme%20learning%20.pdf |
| first_indexed |
2023-09-18T22:38:59Z |
| last_indexed |
2023-09-18T22:38:59Z |
| _version_ |
1777416787309101056 |