Name
Abstract | ||
|---|---|---|
The networks are becoming an essential part of society life and anomalies may represent a loss in network performance. Modeling the traffic behavior pattern is possible to predict the behavior expected and characterize an anomaly. We proposed a hybrid clustering algorithm, Firefly Harmonic Clustering Algorithm (FHCA), for network volume anomaly detection by the combined forces of the algorithms K-Harmonic means (KHM) and Firefly Algorithm (FA). Processing the Digital Signature of Network Segment (DSNS) data and real traffic data, it is possible to detect and point intervals considered anomalous with a trade-off between the 80% true-positive rate and 20% false-positive rate. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICC.2012.6364088 | Communications |
Keywords | Field | DocType |
digital signatures,pattern clustering,telecommunication traffic,DSNS data,FHCA,Firefly harmonic clustering algorithm,K-harmonic means algorithm,digital signature of network segment data,false-positive rate,hybrid clustering algorithm,network volume anomaly detection,real traffic data,traffic behavior pattern modelling,true-positive rate | Data mining,Anomaly detection,CURE data clustering algorithm,Network segment,Computer science,Real-time computing,Artificial intelligence,Cluster analysis,Canopy clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Firefly algorithm | Conference |
ISSN | ISBN | Citations |
1550-3607 E-ISBN : 978-1-4577-2051-2 | 978-1-4577-2051-2 | 2 |
PageRank | References | Authors |
0.38 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mario H. A. C. Adaniya | 1 | 8 | 1.46 |
| Moisés F. Lima | 2 | 2 | 1.39 |
| JOEL J. P. C. RODRIGUES | 3 | 3484 | 341.72 |
| Taufik Abrão | 4 | 126 | 36.18 |
| Mario Lemes Proença Jr. | 5 | 188 | 20.31 |
| Abrao, T. | 6 | 2 | 0.38 |