Using Genetic Algorithm to Improve an Online Response System for Anomaly Traffic by Incremental Mining

Author

Su, Ming-Yang ; Yeh, Sheng-Cheng ; Lin, Chun-Yuen ; Tsai, Chen-Han

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Ming Chuan Univ., Taoyuan, Taiwan

fYear

2010

fDate

6-9 Sept. 2010

Firstpage

582

Lastpage

587

Abstract

This paper presents an online real-time network response system, which can determine whether a LAN is suffering from a flooding attack within a very short time unit. The detection engine of the system is based on the incremental mining of fuzzy association rules from network packets, in which membership functions of fuzzy variables are optimized by a genetic algorithm. The proposed online system belongs to anomaly detection, not misuse detection. Moreover, a mechanism for dynamic firewall updating is embedded in the proposed system for the function of eliminating suspicious connections when necessary.

Keywords

computer network security; data mining; fuzzy set theory; genetic algorithms; local area networks; telecommunication traffic; LAN; anomaly traffic; firewall update; flooding attack; fuzzy association rules; genetic algorithm; incremental mining; online response system; Association rules; Biological cells; Databases; Floods; Genetics; IP networks; Intrusion detection; Fuzzy association rules; anomaly detection; genetic algorithm; membership functions; online incremental mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Parallel and Distributed Processing with Applications (ISPA), 2010 International Symposium on

Conference_Location

Taipei

Print_ISBN

978-1-4244-8095-1

Electronic_ISBN

978-0-7695-4190-7

Type

conf

DOI

10.1109/ISPA.2010.75

Filename

5634386