Name
Title | ||
|---|---|---|
Intrusion detection using a hybridization of evolutionary fuzzy systems and artificial immune systems |
Abstract | ||
|---|---|---|
This paper presents a novel hybrid approach for intrusion detection in computer networks. The proposed approach combines an evolutionary based fuzzy system with an artificial immune system to generate high quality fuzzy classification rules. The performance of final fuzzy classification system has been investigated using the KDD-Cup99 benchmark dataset. The results indicate that in comparison to several traditional techniques, such as C4.5, Naive Bayes, k-NN and SVM, the proposed hybrid approach achieves better classification accuracies for most of the classes of the intrusion detection classification problem. Therefore, the resulted fuzzy classification rules can be used to produce a reliable intrusion detection system. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CEC.2007.4424932 | Singapore |
Keywords | Field | DocType |
artificial immune systems,fuzzy set theory,image classification,security of data,KDD-Cup99 benchmark dataset,artificial immune systems,evolutionary fuzzy systems,fuzzy classification,intrusion detection | Neuro-fuzzy,Artificial immune system,Naive Bayes classifier,Fuzzy classification,Pattern recognition,Computer science,Fuzzy set,Artificial intelligence,Fuzzy control system,Contextual image classification,Intrusion detection system,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-1340-9 | 2 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Saniee Abadeh | 1 | 274 | 21.58 |
| Jafar Habibi | 2 | 387 | 45.06 |
| Maryam Daneshi | 3 | 10 | 1.38 |
| Mojdeh Jalali Heravi | 4 | 4 | 1.78 |
| Maryam Khezrzadeh | 5 | 8 | 0.82 |
| Jalali, M. | 6 | 2 | 0.36 |