Growing hierarchical self-organizing map for filtering intrusion detection alarms
A Network Intrusion Detection System (NIDS) monitors all network actions and generates alarms when it detects suspicious attempts. We present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by a NIDS. Our data mining technique is...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| التنسيق: | conferenceObject |
| منشور في: |
2008
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| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/7860 http://dx.doi.org/10.1109/I-SPAN.2008.42 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/4520211/ |
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| _version_ | 1864513467076050944 |
|---|---|
| author | Mansour, Nashat |
| author2 | Faour, Ahmad Shehab, Maya |
| author2_role | author author |
| author_facet | Mansour, Nashat Faour, Ahmad Shehab, Maya |
| author_role | author |
| dc.creator.none.fl_str_mv | Mansour, Nashat Faour, Ahmad Shehab, Maya |
| dc.date.none.fl_str_mv | 2008 2018-05-18T12:03:10Z 2018-05-18T12:03:10Z 2018-05-18 |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10725/7860 http://dx.doi.org/10.1109/I-SPAN.2008.42 Shehab, M., Mansour, N., & Faour, A. (2008, May). Growing hierarchical self-organizing map for filtering intrusion detection alarms. In Parallel Architectures, Algorithms, and Networks, 2008. I-SPAN 2008. International Symposium on (pp. 167-172). IEEE. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/4520211/ |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE Xplore |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Growing hierarchical self-organizing map for filtering intrusion detection alarms |
| dc.type.none.fl_str_mv | Conference Paper / Proceeding info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/conferenceObject |
| description | A Network Intrusion Detection System (NIDS) monitors all network actions and generates alarms when it detects suspicious attempts. We present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by a NIDS. Our data mining technique is based on a Growing Hierarchical Self-Organizing Map (GHSOM) that adjusts its architecture during an unsupervised training process according to the characteristics of the input alarm data. GHSOM clusters these alarms in a way that supports network administrators in making decisions about true and false alarms. Our empirical results show that our technique is useful for real-world intrusion data. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | LAURepo_457585a8d5654b97e166d30b239bcec1 |
| identifier_str_mv | Shehab, M., Mansour, N., & Faour, A. (2008, May). Growing hierarchical self-organizing map for filtering intrusion detection alarms. In Parallel Architectures, Algorithms, and Networks, 2008. I-SPAN 2008. International Symposium on (pp. 167-172). IEEE. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/7860 |
| publishDate | 2008 |
| publisher.none.fl_str_mv | IEEE Xplore |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
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| spelling | Growing hierarchical self-organizing map for filtering intrusion detection alarmsMansour, NashatFaour, AhmadShehab, MayaA Network Intrusion Detection System (NIDS) monitors all network actions and generates alarms when it detects suspicious attempts. We present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by a NIDS. Our data mining technique is based on a Growing Hierarchical Self-Organizing Map (GHSOM) that adjusts its architecture during an unsupervised training process according to the characteristics of the input alarm data. GHSOM clusters these alarms in a way that supports network administrators in making decisions about true and false alarms. Our empirical results show that our technique is useful for real-world intrusion data.N/AIEEE Xplore2018-05-18T12:03:10Z2018-05-18T12:03:10Z20082018-05-18Conference Paper / Proceedinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://hdl.handle.net/10725/7860http://dx.doi.org/10.1109/I-SPAN.2008.42Shehab, M., Mansour, N., & Faour, A. (2008, May). Growing hierarchical self-organizing map for filtering intrusion detection alarms. In Parallel Architectures, Algorithms, and Networks, 2008. I-SPAN 2008. International Symposium on (pp. 167-172). IEEE.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://ieeexplore.ieee.org/abstract/document/4520211/eninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/78602021-03-19T10:43:07Z |
| spellingShingle | Growing hierarchical self-organizing map for filtering intrusion detection alarms Mansour, Nashat |
| status_str | publishedVersion |
| title | Growing hierarchical self-organizing map for filtering intrusion detection alarms |
| title_full | Growing hierarchical self-organizing map for filtering intrusion detection alarms |
| title_fullStr | Growing hierarchical self-organizing map for filtering intrusion detection alarms |
| title_full_unstemmed | Growing hierarchical self-organizing map for filtering intrusion detection alarms |
| title_short | Growing hierarchical self-organizing map for filtering intrusion detection alarms |
| title_sort | Growing hierarchical self-organizing map for filtering intrusion detection alarms |
| url | http://hdl.handle.net/10725/7860 http://dx.doi.org/10.1109/I-SPAN.2008.42 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/4520211/ |