A hybrid intrusion detection system based on different machine learning algorithms
Recently, Networks have developed quickly during the last many years, and attacks on network infrastructure presently are main threats against network and information security.With quickly growing unauthorized activities in network Intrusion Detection as a part of defense is extremely necessary bec...
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my.uum.repo.120312014-08-25T08:02:01Z http://repo.uum.edu.my/12031/ A hybrid intrusion detection system based on different machine learning algorithms Atefi, Kayvan Yahya, Saadiah Dak, Ahmad Yusri Atefi, Arash QA76 Computer software Recently, Networks have developed quickly during the last many years, and attacks on network infrastructure presently are main threats against network and information security.With quickly growing unauthorized activities in network Intrusion Detection as a part of defense is extremely necessary because traditional firewall techniques cannot provide complete protection against intrusion. There are numerous study in intrusion detection system (IDS) especially with Genetic algorithms (GA) and Support Vector Machine (SVM) but most of them did not get the potential of hybrid SVM using GA. Hence this study aims to hybrid GA and forbids with high accuracy.The paper illustrates the benefit of hybrid SVM via GA also the paper has proven that by enhancing SVM with GA can reduce false alarms and mean square error (MSE) in detecting intrusion. 2013-08-28 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/12031/1/PID22.pdf Atefi, Kayvan and Yahya, Saadiah and Dak, Ahmad Yusri and Atefi, Arash (2013) A hybrid intrusion detection system based on different machine learning algorithms. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28 -30 August 2013, Kuching, Sarawak, Malaysia. http://www.icoci.cms.net.my/proceedings/2013/TOC.html |
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QA76 Computer software Atefi, Kayvan Yahya, Saadiah Dak, Ahmad Yusri Atefi, Arash A hybrid intrusion detection system based on different machine learning algorithms |
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Recently, Networks have developed quickly during the last many years, and attacks on network infrastructure presently are main threats against network and information security.With quickly growing unauthorized activities in network Intrusion Detection as a part of defense is extremely necessary
because traditional firewall techniques cannot provide complete protection
against intrusion. There are numerous study in intrusion detection system (IDS) especially with Genetic algorithms (GA) and Support Vector Machine (SVM) but most of them did
not get the potential of hybrid SVM using GA. Hence this study aims to hybrid GA and forbids
with high accuracy.The paper illustrates
the benefit of hybrid SVM via GA also the paper has proven that by enhancing SVM with GA can reduce false alarms and mean square error (MSE) in detecting intrusion. |
format |
Conference or Workshop Item |
author |
Atefi, Kayvan Yahya, Saadiah Dak, Ahmad Yusri Atefi, Arash |
author_facet |
Atefi, Kayvan Yahya, Saadiah Dak, Ahmad Yusri Atefi, Arash |
author_sort |
Atefi, Kayvan |
title |
A hybrid intrusion detection system based on different machine learning algorithms |
title_short |
A hybrid intrusion detection system based on different machine learning algorithms |
title_full |
A hybrid intrusion detection system based on different machine learning algorithms |
title_fullStr |
A hybrid intrusion detection system based on different machine learning algorithms |
title_full_unstemmed |
A hybrid intrusion detection system based on different machine learning algorithms |
title_sort |
hybrid intrusion detection system based on different machine learning algorithms |
publishDate |
2013 |
url |
http://repo.uum.edu.my/12031/1/PID22.pdf http://repo.uum.edu.my/12031/ http://www.icoci.cms.net.my/proceedings/2013/TOC.html |
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1644280802098282496 |
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13.160551 |