DocumentCode
2616449
Title
Intrusion Detection Using Fuzzy Stochastic Local Search Classifier
Author
Bahamida, Bachir ; Boughaci, Dalila
fYear
2012
fDate
Oct. 27 2012-Nov. 4 2012
Firstpage
111
Lastpage
115
Abstract
This paper proposes a stochastic local search classifier combined with the fuzzy logic concepts for intrusion detection. The proposed classifier works on knowledge base modeled as a fuzzy rule "if-then" and improved by using a stochastic local search. The method is tested on the Benchmark KDD\´99 intrusion dataset and compared with other existing techniques for intrusion detection. The results are encouraging and demonstrate the benefit of the proposed approach.
Keywords
fuzzy set theory; knowledge based systems; pattern classification; search problems; security of data; stochastic processes; benchmark KDD´99 intrusion dataset; fuzzy logic concepts; fuzzy rule if-then; fuzzy stochastic local search classifier; intrusion detection; knowledge base; Bayesian methods; Expert systems; Genetic algorithms; Intrusion detection; Pragmatics; Stochastic processes; DARPA dataset; classification; fuzzy logic; genetic algorithm; intrusion detection; stochastic local search;
fLanguage
English
Publisher
ieee
Conference_Titel
Artificial Intelligence (MICAI), 2012 11th Mexican International Conference on
Conference_Location
San Luis Potosi
Print_ISBN
978-1-4673-4731-0
Type
conf
DOI
10.1109/MICAI.2012.17
Filename
6387224
Link To Document