Learning Vector Quantization and Radial Basis Function Performance Comparison Based Intrusion Detection System

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Information system’s technologies increase rapidly and continuously due to the huge traffic and volume of data. Stored data need to be secured adequately and transferred safely through the computer network. Therefore the data transaction mechanism still exposed to the intrusion attack of which consequences remain unlikable. An intrusion can be understood as a set of actions that can compromise the three security purposes known as Confidentiality, Integrity and Availability (CIA) of resources and services. In order to face on these intrusions, an efficient and robust Intrusion Detection System (IDS) which can detect successfully the intrusion is strongly recommended. An IDS is a network/host security tool used for preventing and detecting malicious attacks which could make a system useless. The purpose of this paper is to implement network intrusion detection system based on machine learning using Artificial Neural Network algorithms specifically the Learning Quantization Vector and Radial Basis Function make the comparison on the performance between these two algorithms.

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With the advancement of internet over years, the number of attacks over internet has also increased. A powerful Intrusion Detection System (IDS) is required to ensure the security of a network. The aim of IDS is to monitor the processes prevailing in a network and to analyze them for signs of any possible deviations. Some studies have been done in this field but a deep and exhaustive work has still not been done. This paper proposes an IDS using machine leaning for network with a good union of feature selection technique and classifier by studying the combinations of most of the popular feature selection techniques and classifiers. A set of significant features is selected from the original set of features using feature selection techniques and then the set of significant features is used to train different types of clas-sifiers to make the IDS. Five folds cross validation is done on NSL-KDD dataset to find results. It is finally observed that K-NN classifier produces better performance than others and, among the feature selection methods, information gain ratio based feature selection method is better.
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Abstract— 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%.
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Intrusion detection system (IDS) is used to produce security alerts to discover attacks against protected network and/or computer systems. IDSs generate high amount of security alerts and analyzing these alert by a security expert are time consuming and error pron. IDS alert management system are used to manage generated alerts and classify true positive and false positives alert. This paper represents an IDS alert management system that uses learning vector quantization technique to classify generated alerts. Because of low classification time per each alert, the system also could be used in active alert management systems.
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