关键词: 有监督学习, 维数约简, 流形学习, Isomap, 可视化, 分类, 入侵检测, ,

Abstract: Intrusion detection is still a hot area in computer security.When performing visualization and classification，the problem should be confronted is the high dimensionality.As one of the manifold learning algorithms Isomap is an effective nonlinear dimension reduction tool.However，when Isomap is applied to the real-world data，it shows some limitations，such as failing to guarantee connectedness of the constructed neighborhood graphs and not using the class labels of the data.An improved version of Isomap，namely S-kv-Isomap，is proposed.S-kv-Isomap utilizes class information to guide the dimension reduction procedure and k-variable method to build connected neighborhood graphs so as to enhance the robustness.The new scheme is evaluated with KDD CUP 1999 datasets in visualization and classification on four kinds of intrusion types： Dos，R2L，Probe，and U2R.Experiment results show that S-kv-Isomap performs best compared with kv-Isomap in visualization.In the classification test，S-kv-Isomap is compared with kv-Isomap，SVM，and k-NN.The results show that S-kv-Isomap performs higher detection rate and very low false positive rate.

Key words: supervised learning, dimension reduction, manifold learning, Isomap, visualization, classification, intrusion detection