WSEAS Transactions on Systems and Control
Print ISSN: 1991-8763, E-ISSN: 2224-2856
Volume 15, 2020
Building a NIDS Using a Two-Stage Classifier and Feature Reduction through Statistical Methods
Authors: , , ,
Abstract: Today, data is produced, transmitted and stored across the internet in abundant quantities. In such a world, modern-day network security systems have had to develop at an equally astounding rate in order to keep up with the data deluge. Subsequently, companies spend a substantial amount of money, time and effort in developing intrusion detection systems to ensure timely detection and prevention of malicious activity in order to preserve system security. In our paper, we propose a two-stage algorithm to solve the problem of NIDS resulting in fewer false positives and false negatives. We built and evaluated the performance of our IDS using the benchmarked NSL-KDD dataset, which consists of 41 features, of which 3 are categorical and the remaining numeric. Categorical features have been transformed via One Hot Encoding due to which the feature space explodes to 122 features. Our aim henceforth is to reduce this feature space through methods of feature selection and dimensionality reduction to develop a computationally inexpensive classifier capable of operating on the reduced feature space using sequential models. The work has involved using autoencoders for feature space reduction followed by a feed forward network, and has delivered encouraging results. We have then extended our analysis to identify features which can be eliminated without any substantial loss of information available for the classification algorithm. The remaining set of features can then be input into a different model to possibly provide better results or reduce training and evaluation time.
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Pages: 102-112
DOI: 10.37394/23203.2020.15.12