Name
Abstract | ||
|---|---|---|
Intrusion Detection based on Adaptive Resonance Theory and Artificial Immune Network Clustering (ID-ARTAINC) is proposed in this paper. First the mass data for intrusion detection are pretreated by Adaptive Resonance Theory (ART) network to form glancing description of the data and to get vaccine. The outputs of ART network are considered as initial antibodies to train an Immune Network, Last Minimal Spanning Tree is employed to perform clustering analysis and obtain characterization of normal data and abnormal data. ID-ARTAINC can deal with mass unlabeled data to distinguish between normal and anomaly and to detect unknown attacks. The computer simulations on the KDD CUP99 dataset show that ID-ARTAINC achieves higher detection rate and lower false positive rate. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11539117_109 | ICNC (2) |
Keywords | Field | DocType |
normal data,abnormal data,intrusion detection,artificial immune network clustering,mass data,immune network,higher detection rate,art network,adaptive resonance theory,mass unlabeled data,computer simulation,false positive rate,cluster analysis,minimal spanning tree | False positive rate,Adaptive resonance theory,Immune network,Computer science,Anomaly-based intrusion detection system,Artificial intelligence,Artificial neural network,Cluster analysis,Intrusion detection system,Machine learning,Minimum spanning tree | Conference |
Volume | ISSN | ISBN |
3611 | 0302-9743 | 3-540-28325-0 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fang Liu | 1 | 92 | 7.30 |
| Lin Bai | 2 | 0 | 0.34 |
| Licheng Jiao | 3 | 5698 | 475.84 |