Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/FUZZY.2011.6007555 | FUZZ-IEEE |
Keywords | Field | DocType |
genetic-fuzzy association rules,fuzzy set theory,network traffic,denial of service attack,fuzzy variables,genetic-fuzzy association rule,network intrusion detection system,denial-of-service (dos) attacks,anomaly detection,incremental mining,genetic algorithm,nids,genetic algorithms,data mining,online system,membership functions,security of data,membership function,association rule,optimization,genetics,association rules,dos attack,databases,denial of service | Data mining,Anomaly detection,Network intrusion detection,Denial-of-service attack,Computer science,Fuzzy logic,Fuzzy set,Association rule learning,Artificial intelligence,Misuse detection,Genetic algorithm,Machine learning | Conference |
ISSN | ISBN | Citations |
1098-7584 E-ISBN : 978-1-4244-7316-8 | 978-1-4244-7316-8 | 1 |
PageRank | References | Authors |
0.35 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming-Yang Su | 1 | 362 | 22.26 |
| Chun-Yuen Lin | 2 | 41 | 3.71 |
| Sheng-Wei Chien | 3 | 1 | 0.68 |
| Han-Chung Hsu | 4 | 1 | 0.35 |