A New Method for Intrusion Detection using Manifold Learning Algorithm

Abstract

Computer and network security has received and will still receive much attention. Any unexpected intrusion will damage the network. It is therefore imperative to detect the network intrusion to ensure the normal operation of the internet. There are many studies in the intrusion detection and intrusion patter recognition. The artificial neural network (ANN) has proven to be powerful for the intrusion detection. However, very little work has discussed the optimization of the input intrusion features for the ANN. Generally, the intrusion features contain a certain number of useless features, which is useless for the intrusion detection. Large dimensions of the feature data will also affect the intrusion detection performance of the ANN. In order to improve the ANN performance, a new approach for network intrusion detection based on nonlinear feature dimension reduction and ANN is proposed in this work. The manifold learning algorithm was used to reduce the intrusion feature vector. Then an ANN classifier was employed to identify the intrusion. The efficiency of the proposed method was evaluated with the real intrusion data. The test result shows that the proposed approach has good intrusion detection performance. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3638