A Hybrid Intrusion Detection System Based on Decision Tree and Support Vector Machine

As the use of network services increase, security is considered as a crucial and major issue in the network. Several computers connected with the network play an essential role in business and other applications running over the network to provide services. Therefore, we need to search out the best ways to protect the system. One of the methods is to provide security to the system and analyze network traffic through intrusion detection or intrusion prevention. In this paper, a hybrid intrusion detection framework is suggested. Proposed hybrid IDS is a combination of two machine learning algorithms J48 DT and SVM. To select relevant features from the KDD CUP dataset Particle Swarm Optimization is used. WEKA is used to implement classification on the KDD CUP dataset. The dataset is divided into ratios of 60:40, 70:30, and 80:20 for training and testing purpose. The experiment result showed 99.1% accuracy, 99.6% detection rate and 1.0% FAR for 60:40 datasets whereas accuracy, detection rate and FAR for 70:30 datasets are 99.2%, 99.6% and 0.9% respectively for 80:20 datasets 99.1%, 99.6% and 0.9% respectively.