Network intrusion detection based on weighted support vector machine
Zhihua Li · Jisuanji gongcheng yu sheji · 2007
In the network intrusion detection,when the use of training sets with uneven class sizes results in classification biases towards the class with the large training size.The main causes lie in that the penalty of misclassification for each training sample is considered equally.Weighted support vector machines for classification where penalty of misclassification for each training sample is different,and then the classification accuracy for the class with small training size is improved,and overcomes the drawback which standard support vector machinen algorithm can not deal with this sample flexibly.But this improvement is obtained at the cost of the possible decrease of classification accuracy for the class with large training size and the possible decrease of the total classification accuracy.This introduce it to network intrusion detection,the experiment results prove it is effective and efficient.