Semi-Supervised Learning-Based Network Intrusion Detection System
Jiang Mei · Computer Technology and Development · 2011
In the intrusion detection method,semi-supervised learning as a special form of learning,combines the advantages of supervised learning and unsupervised learning in detecting the known and unknown mode of data.Accordingly,to improve the detection accuracy,proposed a semi-supervised intrusion detection model that integrates the respective advantages of SVM and KMO(online k-means).In this model,firstly use the SVM algorithm to filter all the input data,then the considered legitimate data is classified with KMO,so the decision-making module can respond the final input data.Experiments show that the model has a higher detection accuracy than use each of them alone.Thus,the model has practical value for real intrusion detection system.