Lisehroodi, Mazyar Mohammadi and Muda, Zaiton and Yassin, Warusia (2013) A hybrid framework based on neural network MLP and means clustering for intrusion detection system. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28 -30 August 2013, Kuching, Sarawak, Malaysia.

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	ISBN: 9789832078791 Organized by: Universiti Utara Malaysia
Uncontrolled Keywords:	intrusion detection system, K-means clustering , neural network classifier, Multi-Layer Perceptron
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	College of Arts and Sciences
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	25 Aug 2014 07:00
Last Modified:	25 Aug 2014 07:00
URI:	https://repo.uum.edu.my/id/eprint/12030

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