A Membership Function for Feature Clustering Based Network Intrusion and Anomaly Detection

Abstract

Detecting Intrusions and anomalies is becoming much more challenging with new attacks popping out over a period of time. Achieving better accuracies applying algorithms used for identifying intrusions and anomalies has several hidden data mining challenges. Although neglected by many research findings, one of the most important and biggest challenges is the similarity computation. Another challenge that cannot be simply neglected is the number of features that attributes to dimensionality. This research aims to come up with a new membership function to carry similarity computation that can be helpful for addressing feature dimensionality issues. In principle, this work is limited at introducing a novel membership function that can help to achieve better classification accuracies and eventually lead to better intrusion and anomaly detection.