A hybrid framework based on neural network MLP and K-means clustering for intrusion detection system

• Main Authors: Lisehroodi, Mazyar Mohammadi, Muda, Zaiton, Yassin, Warusia
• Format: Conference or Workshop Item
• Language: English
• Published: UUM College of Arts and Sciences, Universiti Utara Malaysia 2013
• 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
• Institution: Universiti Putra Malaysia

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.