Fuzzy Rough Classification Models for Network Intrusion Detection

Authors

  • Ashalata Panigrah Department of Computer Science & Engineering, SMIT, Berhampur
  • Manas Ranjan Patra Department of Computer Science, Berhampur University, Berhampur, India

DOI:

https://doi.org/10.14738/tmlai.42.1882

Keywords:

FNN, Fuzzy-Rough NN, FRONN, VQNN, OWANN

Abstract

In recent years advancements in communication technology have led to a wide range of Internet services. While an overwhelming number of Internet users have shown interest in such services, incidences of cyber-attacks by miscreants have thwarted their dependence on electronically-accessible services. In order to deal with this alarming situation intrusion detection systems (IDS) have emerged as a potential solution to analyse network activities of users and report attempts of possible intrusions. Building an intrusion detection system is a complex and challenging task. This requires analysis of network data from several dimensions so as to develop a pragmatic system to handle different forms of intrusive behaviour of attackers. In this paper, we propose a hybrid intrusion detection approach which combines techniques based on both fuzzy and rough set theories to classify network data as normal and anomalous. Our approach comprises of two phases; in the first phase the most relevant features are extracted using a set of rank and search based methods; and in the second phase we classify the reduced dataset as normal or anomalous using five different classifiers, namely, Fuzzy Nearest Neighbour, Fuzzy-Rough Nearest Neighbour, Fuzzy-Rough Ownership NN, Vaguely Quantified Nearest Neighbours, and Ordered Weighted Average Nearest Neighbours. Experimental results show that the proposed hybrid approach has the ability to achieve high intrusion detection rate and low false alarm

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Published

2016-05-01

How to Cite

Panigrah, A., & Patra, M. R. (2016). Fuzzy Rough Classification Models for Network Intrusion Detection. Transactions on Machine Learning and Artificial Intelligence, 4(2), 07. https://doi.org/10.14738/tmlai.42.1882