Intrusion Detection System Using Semi-Supervised Machine Learning By DBSCAN
A lot of paper has been done in the area of internet traffic classification by application type and several classifiers are suggested. In this paper, the authors apply both technique i.e. supervised and unsupervised learning approach, known as semi-supervised classification based on DBSCAN clustering algorithm. It classifies network flows by using only flow statistics. This methodology is based on machine learning principle, consists of two components: clustering and classification. The goal of clustering is to partitions the training data set in to disjoint groups.