A hybrid framework based on neural network MLP and K-means clustering for intrusion detection system
Due to the widespread use of Internet and communication networks, in case a reliable and secure network plays a crucial role for information technology (IT) service providers and users. The hardness of network attacks, as well as their complexity, has also increased lately. High false alarm rate is...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
UUM College of Arts and Sciences, Universiti Utara Malaysia
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/41332/ http://psasir.upm.edu.my/id/eprint/41332/1/41332.pdf |
| _version_ | 1848849667318611968 |
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| author | Lisehroodi, Mazyar Mohammadi Muda, Zaiton Yassin, Warusia |
| author_facet | Lisehroodi, Mazyar Mohammadi Muda, Zaiton Yassin, Warusia |
| author_sort | Lisehroodi, Mazyar Mohammadi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Due to the widespread use of Internet and communication networks, in case a reliable and secure network plays a crucial role for information technology (IT) service providers and users. The hardness of network attacks, as well as their complexity, has also increased lately. High false alarm rate is a big issue for majority of researches in this area. To overwhelm this challenge a hybrid learning approach is proposed, employing the combination of K-means clustering and Neural Network Multi-Layer Perceptron (MLP) classification. Concerning the robustness of K-means method and MLP algorithms benefits, this research is the part of an effort to develop a hybrid information detection system (IDS) which is able to detect high percentage of novel attacks while keep the false alarm at low rate. This paper provides the conceptual view and a general framework of the proposed system. |
| first_indexed | 2025-11-15T09:54:02Z |
| format | Conference or Workshop Item |
| id | upm-41332 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:54:02Z |
| publishDate | 2013 |
| publisher | UUM College of Arts and Sciences, Universiti Utara Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-413322015-11-04T07:29:51Z http://psasir.upm.edu.my/id/eprint/41332/ A hybrid framework based on neural network MLP and K-means clustering for intrusion detection system Lisehroodi, Mazyar Mohammadi Muda, Zaiton Yassin, Warusia Due to the widespread use of Internet and communication networks, in case a reliable and secure network plays a crucial role for information technology (IT) service providers and users. The hardness of network attacks, as well as their complexity, has also increased lately. High false alarm rate is a big issue for majority of researches in this area. To overwhelm this challenge a hybrid learning approach is proposed, employing the combination of K-means clustering and Neural Network Multi-Layer Perceptron (MLP) classification. Concerning the robustness of K-means method and MLP algorithms benefits, this research is the part of an effort to develop a hybrid information detection system (IDS) which is able to detect high percentage of novel attacks while keep the false alarm at low rate. This paper provides the conceptual view and a general framework of the proposed system. UUM College of Arts and Sciences, Universiti Utara Malaysia 2013 Conference or Workshop Item NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/41332/1/41332.pdf Lisehroodi, Mazyar Mohammadi and Muda, Zaiton and Yassin, Warusia (2013) A hybrid framework based on neural network MLP and K-means clustering for intrusion detection system. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28-30 Aug. 2013, Sarawak, Malaysia. (pp. 305-311). http://www.icoci.cms.net.my/proceedings/2013/PDF/PID20.pdf |
| spellingShingle | Lisehroodi, Mazyar Mohammadi Muda, Zaiton Yassin, Warusia A hybrid framework based on neural network MLP and K-means clustering for intrusion detection system |
| title | A hybrid framework based on neural network MLP and K-means clustering for intrusion detection system |
| title_full | A hybrid framework based on neural network MLP and K-means clustering for intrusion detection system |
| title_fullStr | A hybrid framework based on neural network MLP and K-means clustering for intrusion detection system |
| title_full_unstemmed | A hybrid framework based on neural network MLP and K-means clustering for intrusion detection system |
| title_short | A hybrid framework based on neural network MLP and K-means clustering for intrusion detection system |
| title_sort | hybrid framework based on neural network mlp and k-means clustering for intrusion detection system |
| url | http://psasir.upm.edu.my/id/eprint/41332/ http://psasir.upm.edu.my/id/eprint/41332/ http://psasir.upm.edu.my/id/eprint/41332/1/41332.pdf |