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...
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| 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/41332/1/41332.pdf http://psasir.upm.edu.my/id/eprint/41332/ http://www.icoci.cms.net.my/proceedings/2013/PDF/PID20.pdf |
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| Institution: | Universiti Putra Malaysia |
| Language: | English |
| Summary: | 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. |
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