Network Intrusion Detection system based on Feature Selection and Triangle area Support Vector Machine

摘要

As the cost of the data processing and Internet accessibility increases, more and more organizations are be-coming vulnerable t o a wide range of cyber threats. Most current offline intrusio n detect ion systems are focused on unsupervised and supervised machine learning approaches. Existing model has high error rate during the at tack classification using support vector machine learning algorithm. Besides, with the study of existing work, feature selection techniques are also essential to improve high ef ficiency and effect iveness. Performance of different types of attacks detection should also be improved and evaluated using t he proposed approach. In t his proposed system, Information Gain (IG) and Triangle Area based KNN are used for selecting more discriminative feat ures by combining Greedy k-means clustering algorithm and SVM classifier to detect Network attacks. This system achieves high accuracy detection rat e and less error rate of KDD CUP 1999 training data set.