Attack’s Feature Selection-Based Network Intrusion Detection System Using Fuzzy Control Language
| Autor: | S. Devaraju, S. Ramakrishnan |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Java
Fuzzy Control Language Computer science business.industry Anomaly-based intrusion detection system 020206 networking & telecommunications Computational intelligence Feature selection 02 engineering and technology computer.software_genre Machine learning Fuzzy logic Theoretical Computer Science Computational Theory and Mathematics Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Entropy (information theory) 020201 artificial intelligence & image processing Data mining False positive rate Artificial intelligence business computer Software computer.programming_language |
| Zdroj: | International Journal of Fuzzy Systems. 19:316-328 |
| ISSN: | 2199-3211 1562-2479 |
| DOI: | 10.1007/s40815-016-0160-6 |
| Popis: | Network intrusion detection system has wide range of disputes due to lack of security over the networks. The network intrusion detection system must be reliable to detect the emerging threats over the networks and perform effectively, efficiently to manage large amount of traffic. This paper proposes entropy-based feature selection to select the important features, layered fuzzy control language to generate fuzzy rules, and layered classifier to detect various network attacks namely neptune, smurf, back, and mailbomb. Layered classifier has improved the performances and reduces the computational time. KDD Dataset which consists of three components, namely “Corrected Dataset,” “10 % Dataset,” and “Full Dataset,” are employed to evaluate the performances of the proposed system. The experiments are carried out using an open source java library called jFuzzyLogic and the results show considerable improvement in the detection rate, reduce the false positive rate, significant improvement in recall value and reduces the computational time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink