Fuzzy Rough Set Based Network Intrusion Detection with Wrapper Subset Evaluator
Journal Title: International Journal of Engineering and Science Invention - Year 2018, Vol 7, Issue 2
Abstract
With the growing penetration of Internet, concerns of cyber security is looming high world-wide. In one hand there is enthusiasm to develop internet based applications for wider access, on the other hand seriousefforts are being made to evolve techniques to deal with the menace of cyber-attacks. One of the research areasfocusses on developing techniques to analyze available network intrusion data and build intrusion detection models which can be used effectively to raise alerts whenever suspicious user behavior is observed in a network. In this work, we propose a soft computing based technique to construct an intrusion detection model using five classifiers, namely, Fuzzy Nearest Neighbour (FNN), Fuzzy-Rough Nearest Neighbour (FRNN), Fuzzy-Rough Ownership Nearest Neighbour (FRONN), Vaguely Quantified Nearest Neighbour (VQNN), and Ordered Weighted Average Nearest Neighbour (OWANN). Further, the most relevant features in the input data have been extracted through a preprocessing stage using the wrapper subset evaluator. Finally, the performance of the model has been evaluated on the NSL-KDD intrusion dataset in terms of accuracy, precision, detection rate, and false alarm rate
Authors and Affiliations
Ashalata Panigrahi, Manas Ranjan Patra
Keywords
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