Intrusion Detection using Support Vector Machine with Feature Reduction Techniques

The intrusion detection system plays an important role in securing our system, by preventing our system from intruders. However, traditional intrusion detection such as user authentication, encryption and firewall have failed to completely protect networks and systems from the increasing and sophisticated attacks and malwares. Our new method is to classify network behaviour as normal or abnormal while reducing misclassification. Support Vector Machine (SVM) is a technique for detecting intrusions in the system, which can provide real-time detection capability and it can deal with large dimensionality of data. SVM can learn a larger set of patterns and be able to scale better, because the classification complexity does not depend on the dimensionality of the feature space. In this paper, we combine This algorithm is implemented and evaluated using standard benchmark KDDCUP99 data set.