Network intrusion classification based on semi-supervised learning
Jianhu Zhao · Jisuanji yingyong yanjiu · 2014
In order to solve the problem that it costs too much to obtain labeled intrusion data in the network environment,semi-supervised learning is applied into the field of network intrusion.According to the different types of network attack,this paper divided the limited labeled intrusion data into three equal training sets to form three different classifiers.Through training learning by three single classifiers,the unlabeled samples were labeled.It introduced the process of using KDD Cup 99 data sets to construct semi-supervised classification experiment data sets.The experimental results show that semi-supervised learning can effectively dig the unlabeled samples information of intrusion data and has a higher rate of intrusion classification.