Entropy-Based Feature Selection for Network Anomaly Detection

Abstract

Over the decades, ensuring security of networks has been a major concern; the open-ended nature of networks though promoting its ease of use and connectivity has also contributed its security challenges. The growth in technology fosters the developments of varies gadgets but this growth has also been harnessed by intruders to develop new and more sophisticated ways of breaking into a network, hence the need for continual research in developing more sophisticated methods that can detect new day attacks in a various types of networks. Different algorithms have been used in building network security systems; however, in spite of their effectiveness, these algorithms also have different disadvantages that reduce its performance. A major way of improving the performance is through feature selection. This research focuses on how feature selection methods can be used to improve the performance of the minimum distance classifier using the UNB/IDS 2012 dataset. The performance of the classifier is optimized using two basic feature selection methods: entropy and variance, then a k-fold cross validation is performed to validate the accuracy results.