A new intrusion detection system based on fast learning network and particle swarm optimization

Supervised Intrusion Detection System is a system that has the capability of learning from examples about previous attacks to detect new attacks. Using ANN based intrusion detection is promising for reducing the number of false negative or false positives because ANN has the capability of learning f...

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Main Authors: Ali, Mohammed Hasan, Mohamad Fadli, Zolkipli, Al Mohammed, B.A.D., Alyani, Ismail
Format: Article
Language:English
Published: IEEE 2018
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/22096/1/08326489.pdf
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author Ali, Mohammed Hasan
Mohamad Fadli, Zolkipli
Al Mohammed, B.A.D.
Alyani, Ismail
author_facet Ali, Mohammed Hasan
Mohamad Fadli, Zolkipli
Al Mohammed, B.A.D.
Alyani, Ismail
author_sort Ali, Mohammed Hasan
collection UMP
description Supervised Intrusion Detection System is a system that has the capability of learning from examples about previous attacks to detect new attacks. Using ANN based intrusion detection is promising for reducing the number of false negative or false positives because ANN has the capability of learning from actual examples. In this article, a developed learning model for Fast Learning Network (FLN) based on particle swarm optimization(PSO) has been proposed and named as PSO-FLN. The model has been applied to the problem of intrusion detection and validated based on the famous dataset KDD99. Our developed model has been compared against a wide range of meta-heuristic algorithms for training ELM, and FLN classifier. PSO-FLN has outperformed other learning approaches in the testing accuracy of the learning.
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spelling UMPir220962018-09-24T06:14:16Z http://umpir.ump.edu.my/id/eprint/22096/ A new intrusion detection system based on fast learning network and particle swarm optimization Ali, Mohammed Hasan Mohamad Fadli, Zolkipli Al Mohammed, B.A.D. Alyani, Ismail QA75 Electronic computers. Computer science T Technology (General) Supervised Intrusion Detection System is a system that has the capability of learning from examples about previous attacks to detect new attacks. Using ANN based intrusion detection is promising for reducing the number of false negative or false positives because ANN has the capability of learning from actual examples. In this article, a developed learning model for Fast Learning Network (FLN) based on particle swarm optimization(PSO) has been proposed and named as PSO-FLN. The model has been applied to the problem of intrusion detection and validated based on the famous dataset KDD99. Our developed model has been compared against a wide range of meta-heuristic algorithms for training ELM, and FLN classifier. PSO-FLN has outperformed other learning approaches in the testing accuracy of the learning. IEEE 2018-03-27 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22096/1/08326489.pdf Ali, Mohammed Hasan and Mohamad Fadli, Zolkipli and Al Mohammed, B.A.D. and Alyani, Ismail (2018) A new intrusion detection system based on fast learning network and particle swarm optimization. IEEE Access, 6. 20255 -20261. ISSN 2169-3536. (Published) https://ieeexplore.ieee.org/document/8326489/ 10.1109/ACCESS.2018.2820092
spellingShingle QA75 Electronic computers. Computer science
T Technology (General)
Ali, Mohammed Hasan
Mohamad Fadli, Zolkipli
Al Mohammed, B.A.D.
Alyani, Ismail
A new intrusion detection system based on fast learning network and particle swarm optimization
title A new intrusion detection system based on fast learning network and particle swarm optimization
title_full A new intrusion detection system based on fast learning network and particle swarm optimization
title_fullStr A new intrusion detection system based on fast learning network and particle swarm optimization
title_full_unstemmed A new intrusion detection system based on fast learning network and particle swarm optimization
title_short A new intrusion detection system based on fast learning network and particle swarm optimization
title_sort new intrusion detection system based on fast learning network and particle swarm optimization
topic QA75 Electronic computers. Computer science
T Technology (General)
url http://umpir.ump.edu.my/id/eprint/22096/1/08326489.pdf
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