  In this paper, we analyze existing feature selection methods to identify the
key elements of network traffic data that allow intrusion detection. In
addition, we propose a new feature selection method that addresses the
challenge of considering continuous input features and discrete target values.
We show that the proposed method performs well against the benchmark selection
methods. We use our findings to develop a highly effective machine
learning-based detection systems that achieves 99.9% accuracy in distinguishing
between DDoS and benign signals. We believe that our results can be useful to
experts who are interested in designing and building automated intrusion
detection systems.
