Title :
Genetic-fuzzy association rules for network intrusion detection systems
Author :
Su, Ming-Yang ; Lin, Chun-Yuen ; Chien, Sheng-Wei ; Hsu, Han-Chung
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Ming Chuan Univ., Taoyuan, Taiwan
Abstract :
A network intrusion detection system (NIDS) based on genetic-fuzzy association rules is presented in the paper, which mines rules in an incremental manner in order to meet the real-time requirement of a NIDS. More precisely, the proposed NIDS adopts the incremental mining of fuzzy association rules from network traffic, in which membership functions of fuzzy variables are optimized by a genetic algorithm. The proposed online system belongs to anomaly detection, not misuse detection. Some denial-of-service (DoS) attacks were experimented in this study to show the performance of the proposed NIDS. The results show that the proposed NIDS can detect DoS attacks in both effectiveness and efficiency.
Keywords :
data mining; fuzzy set theory; genetic algorithms; security of data; NIDS; anomaly detection; denial of service attack; fuzzy variables; genetic algorithm; genetic-fuzzy association rule; incremental mining; membership functions; network intrusion detection system; network traffic; online system; Association rules; Biological cells; Databases; Genetic algorithms; Genetics; IP networks; Optimization; Genetic-fuzzy association rules; anomaly detection; denial-of-service (DoS) attacks; genetic algorithm; incremental mining; membership functions;
Conference_Titel :
Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7315-1
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2011.6007555
