Semi-supervised learning methods for network intrusion detection
Chuanliang Chen, Yunchao Gong, Yingjie Tian · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · 2008
Recently increasing interests of applying or developing specialized machine learning techniques have attracted many researchers in the intrusion detection community. Existing research work show: the supervised algorithms deteriorates signifycantly if unknown attacks are present in the test data; the unsupervised algorithms exhibit no significant difference in performance between known and unknown attacks but their performances are not that satisfying. In this contribution, we propose two semi-supervised classification methods, Spectral Graph Transducer and Gaussian Fields Approach, to detect unknown attacks and one semi-supervised clustering method—MPCK-means to improve the performances of the traditional purely unsupervised clustering methods. Our empirical study shows that performances of semi-supervised classification methods are much better than those of supervised classifiers, and semi-supervised clustering method can improve purely unsupervised clustering methods markedly.