Feature Selection Using Relative Fuzzy Entropy and Ant Colony Optimization Applied to Real-time Intrusion Detection System

Intrusion Detection System (IDS) is one of the most important component of network defense mechanism. In an attempt to detect network attacks, network traffic features need to be identified and both attack and normal data need to be profiled. This paper proposes a set of network traffic features tha...

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Bibliographic Details
Published inProcedia computer science Vol. 85; pp. 503 - 510
Main Authors Varma, P. Ravi Kiran, Kumari, V. Valli, Kumar, S. Srinivas
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2016
Subjects
Ant Colony Optimization
Feature selection
Fuzzy Entropy
Fuzzy Rough sets
Real-Time Intrusion Detection
Ant Colony Optimization
Fuzzy Entropy
Feature selection
Real-Time Intrusion Detection
Fuzzy Rough sets
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Summary:Intrusion Detection System (IDS) is one of the most important component of network defense mechanism. In an attempt to detect network attacks, network traffic features need to be identified and both attack and normal data need to be profiled. This paper proposes a set of network traffic features that can be extracted for Real-Time Intrusion Detection. This paper also proposes Fuzzy Entropy based heuristic for Ant Colony Optimization (ACO) in-order to search for global best smallest set of network traffic features for Real-Time Intrusion Detection Data set. The proposed feature reduction algorithm was tested on standard bench-mark UCI data sets, and found to be efficient. Further the algorithm was applied to Real-Time IDS data set and found to produce promising results.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2016.05.203