Name
Abstract | ||
|---|---|---|
Current Network Behavior Analysis (NBA) techniques are based on anomaly detection principles and therefore subject to high error rates. We propose a mechanism that deploys trust modeling, a technique for cooperator modeling from the multi-agent research, to improve the quality of NBA results. Our system is designed as a set of agents, each of them based on an existing anomaly detection algorithm coupled with a trust model based on the same traffic representation. These agents minimize the error rate by unsupervised, multi-layer integration of traffic classification. The system has been evaluated on real traffic in Czech academic networks. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87403-4_25 | RAID |
Keywords | Field | DocType |
improving anomaly detection error,deploys trust modeling,traffic representation,traffic classification,cooperator modeling,error rate,real traffic,anomaly detection principle,collective trust modeling,nba result,existing anomaly detection algorithm,high error rate,behavior analysis,anomaly detection | Traffic classification,Data mining,Anomaly detection,Computer science,Computer security,Word error rate,Network behavior | Conference |
Citations | PageRank | References |
2 | 0.42 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Rehak | 1 | 251 | 28.57 |
| Michal Pĕchouček | 2 | 2 | 1.77 |
| Karel Bartos | 3 | 110 | 12.60 |
| Martin Grill | 4 | 101 | 10.79 |
| Pavel Čeleda | 5 | 14 | 2.96 |
| Vojtech Krmicek | 6 | 47 | 5.75 |