Feature Selection using Attribute Ratio in NSL-KDD data
2014
In Recent year, network traffic is quickly increasing due to the development of information and communication technology and the spread of smart device. Most of intrusion detection studies have focused on efficient intrusion detection method. Feature selection research is important because we detect intrusions or attacks after filtering irrelevant or redundant features in network traffic data. The purpose of this study is to identify important selected input features in building IDS that is computationally efficient and effective. We apply one of the efficient classifier decision tree algorithm for evaluating feature reduction method. We compare between proposed method and other standard methods.