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Information Technology Journal
  Year: 2012 | Volume: 11 | Issue: 7 | Page No.: 794-798
DOI: 10.3923/itj.2012.794.798
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An Intrusion Detection Model Based on GS-SVM Classifier

Xiangyu Lei and Ping Zhou

The Coarse-to-Refined Grid Search Support Vector Machine (GS-SVM) is an improvement on One-class Support Vector Machine (SVM). This study provides a solution for Intrusion Detection System (IDS) based on support vector machine. In practice, it is inefficient for SVM to identify massive intrusive behaviors which will exhaust memory resources. In addition, the accuracy of classification is subject to data preprocessing and parameter selection. In order to obtain precise detection rate, it is crucial to optimize the related parameters for proper kernel function. For this reason, an optimization algorithm based on grid search is proposed. Experiments over networks connection records from KDD 99 data set are implemented for 1-vs-N SVM to evaluate the proposed method. This approach reduces the training time, accelerates the speed of Cross Validation and improves the adaptability of the IDS.
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  •    Improving Intrusion Detection Using Genetic Algorithm
  •    The Research of Ear Recognition Based on Gabor Wavelets and Support Vector Machine Classification
  •    Minimax Probability Machine with Genetic Feature Optimized for Intrusion Detection
  •    A Radial Basic Function with Multiple Input and Multiple Output Neural Network to Control a Non-Linear Plant of Unknown Dynamics
  •    PCA-ICA Ensembled Intrusion Detection System by Pareto-Optimal Optimization
  •    Multi-Resolution Signal Decomposition and Approximation Based on SVMS
How to cite this article:

Xiangyu Lei and Ping Zhou, 2012. An Intrusion Detection Model Based on GS-SVM Classifier. Information Technology Journal, 11: 794-798.

DOI: 10.3923/itj.2012.794.798


28 March, 2012
I have project about intrusion detection sysytrem using kddcup99 data set Dos attack




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