Network intrusion detection method based on novel support vector machine
Shitong Wang · Jisuanji gongcheng yu sheji · 2008
Oneof the approaches to train support vector machine(SVM) for large-scale intrusion detection problem is the decomposition method.A new method based on the quantum-behaved particle swarm optimization(QPSO) is developedtosolve quadratic programming(QP) problem,and to find the optimal solution.ArraySVM algorithm is also improved to train KDD intrusion detection data sets.Based on the experimental results comparison and analysis,the present method is shown to more complete than previous ArraySVM algorithm in describing the modified algorithm precision,and also to reduce the number of support vector points.