Intrusions Detection based on Optimum Features Subset and Efficient Dataset Selection
Hafiz Muhammad Imran, Azween B. Abdullah, Sellappan Palaniappan · 2012
Intrusion Detection Systems are security programs to decide whether events and activities occurring in a system or network are intrusive or legitimate. The objective of IDS is to identify intrusions with low false alarms and high detection rate while consuming lesser resources. There are plentiful issues in traditional IDS including regular updating, low detection capability to unknown attacks, non-adapting high false alarms rate, high resources consumption and many others. Similarly, Intelligent Network IDS have snags of performance efficiency, false positive and false negative while today's advance Neural Network approaches are also facing training/learning overhead, high false alarms and low detection rate. Soft computing is an innovative field to develop intelligent IDS while minimizing the deficiencies in other approaches. In this paper, an efficient soft computing approach is proposed by selecting an optimum subset of features. For training and testing of system, NSL-KDD dataset is preferred over KDD-Cup as there are approved deficits in KDD-Cup. Features transformation and optimum subset selection is done by Linear Discriminant Analysis (LDA) algorithm and Genetic Algorithm (GA) respectively. Radial Basis Function (RBF) is adapted as features classifier. Empirical results show that the new proposed system gives better and robust representation of an ideal intrusion detection system while having the reduced number of features, low false alarms, high detection rate and minimum computation cost.