Name
Title | ||
|---|---|---|
Large-scale IP network behavior anomaly detection and identification using substructure-based approach and multivariate time series mining |
Abstract | ||
|---|---|---|
In this paper, a substructure-based network behavior anomaly detection approach, called WFS (Weighted Frequent Subgraphs),
is proposed to detect the anomalies of a large-scale IP networks. With application of WFS, an entire graph is examined, unusual
substructures of which are reported. Due to additional information given by the graph, the anomalies are able to be detected
more accurately. With multivariate time series motif association rules mining (MTSMARM), the patterns of abnormal traffic
behavior are able to be obtained. In order to verify the above proposals, experiments are conducted and, together with application
of backbone networks (Internet2) Netflow data, show some positive results. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s11235-010-9384-1 | Telecommunication Systems |
Keywords | Field | DocType |
Anomaly detection and identification,Weighted frequent subgraphs,Multivariate time series motif association rules mining | Graph,Data mining,Anomaly detection,Pattern recognition,NetFlow,Computer science,Multivariate statistics,Internet protocol suite,Association rule learning,Artificial intelligence,Network behavior,Substructure | Journal |
Volume | Issue | ISSN |
44 | 3-4 | 1018-4864 |
Citations | PageRank | References |
8 | 0.57 | 44 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weisong He | 1 | 8 | 1.24 |
| Guangmin Hu | 2 | 15 | 5.84 |
| Yingjie Zhou | 3 | 52 | 11.57 |