Title :
Using Fuzzy Expert System Based on Genetic Algorithms for Intrusion Detection System
Author_Institution :
City Coll., Sch. of Comput. & Comput. Sci., Zhejiang Univ., Hangzhou, China
Abstract :
Fuzzy logic based methods together with the techniques from Artificial Intelligence have gained importance. Association rules together with fuzzy logic to model the fuzzy association rules are being used for classifying data. These together with the techniques of genetic algorithms like genetic programming are producing better results. Therefore, in this article, we firstly analyze the current situation of Intrusion Detection Systems, then raise a genetics-based fuzzy system algorithm. In the first stage of this algorithm, it draws initial rules out by using fuzzy algorithm, and in the second stage, the membership function is optimized by the genetic algorithm, with simplification of fuzzy rules, to build a high performance fuzzy system. Finally, we apply this algorithm to the Intrusion Detection System and get a better performance.
Keywords :
data mining; expert systems; fuzzy logic; genetic algorithms; security of data; artificial intelligence; association rules; fuzzy expert system; fuzzy logic; genetic algorithms; genetic programming; intrusion detection system; membership function; Biological cells; Computer errors; Fuzzy logic; Fuzzy sets; Fuzzy systems; Genetic algorithms; Hybrid intelligent systems; Information technology; Intrusion detection; Protection; fuzzy rule; genetic algorithm; intrusion detection; membership function; rule extraction;
Conference_Titel :
Information Technology and Applications, 2009. IFITA '09. International Forum on
Conference_Location :
Chengdu
Print_ISBN :
978-0-7695-3600-2
DOI :
10.1109/IFITA.2009.107