%0 Journal Article
%T Learning Vector Quantization (LVQ) and k-Nearest Neighbor for Intrusion Classification
%A Reyadh Shaker Naoum
%A Zainab Namh Al-Sultani
%J World of Computer Science and Information Technology Journal
%D 2012
%I 
%X Attacks on computer infrastructure are becoming an increasingly serious problem nowadays, and with the rapid expansion of computer networks during the past decade, computer security has become a crucial issue for protecting systems against threats, such as intrusions. Intrusion detection is an interesting approach that could be used to improve the security of network system. Different soft-computing based methods have been proposed in recent years for the development of intrusion detection systems. This paper presents a composition of Learning Vector Quantization artificial neural network and k-Nearest Neighbor approach to detect intrusion. A Supervised Learning Vector Quantization (LVQ) was trained for the intrusion detection system; it consists of two layers with two different transfer functions, competitive and linear. Competitive (hidden) and output layers contain a specific number of neurons which are the sub attack types and the main attack types respectively. k-Nearest Neighbor (kNN) as a machine learning algorithm was implemented using different distance measures and different k values, but the results demonstrates that using the first norm instead the second norm and using k=1 gave the best results among other possibilities. The experiments and evaluations of the proposed method have been performed using the NSL-KDD 99 intrusion detection dataset. Hybrid (LVQ_kNN) was able to classify the datasets into five classes at learning rate 0.09 using 23 hidden neurons with classification rate about 89%.
%K Intrusion Detection System
%K Learning Vector Quantization
%K k-Nearest Neighbor.
%U http://www.wcsit.org/pub/2012/vol.2no.3/Learning%20Vector%20Quantization%20(LVQ)%20and%20k-Nearest%20Neighbor%20for%20Intrusion%20Classification.pdf