Name
Title | ||
|---|---|---|
Clustering of Network Link Characteristic for Detector Placement of Macroscopical Prewarning |
Abstract | ||
|---|---|---|
Network-based and distributed intrusion detection system is aroused by the burst-outs of large-scale abnormal events. How to place detection instruments is the key to the detections. The paper turned the problem of detector placement to that of the clustering of topology graph. A novel Bidirectional Hierarchical Clustering algorithm is put forward, which decreases the amount of result clusters by integration of initial marker selection method based on node out-degree. The simulation results demonstrate that our clustering approaches effectively by comparison on the three evaluations metric, Diameter Stability, Average Advantage Ratio and Average Coefficient of Variation. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IMSCCS.2006.198 | IMSCCS (2) |
Keywords | Field | DocType |
detector placement,diameter stability,detection instrument,clustering approach,average advantage ratio,average coefficient,macroscopical prewarning,node out-degree,initial marker selection method,network link characteristic,intrusion detection system,large-scale abnormal event,clustering algorithms,switches,detectors,coefficient of variation,graph theory,hierarchical clustering,stability analysis,intrusion detection,spine,network topology,topology,internet | k-medians clustering,Hierarchical clustering,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Hierarchical clustering of networks,Artificial intelligence,Cluster analysis,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-2581-4 | 1 | 0.36 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui He | 1 | 80 | 16.45 |
| Ming-zeng Hu | 2 | 180 | 23.89 |
| Hongli Zhang | 3 | 267 | 41.85 |