Name
Abstract | ||
|---|---|---|
With the prevalence of multi-user environments, it has become an increasingly challenging task to precisely identify who is doing what on an enterprise network. Current management systems that rely on inferring user identity and application usage via log files from routers and switches are not capable of accurately reporting and managing a large-scale network due to the coarseness of the collected data. We propose a system that utilizes finer-grained data in the form of local context, i.e., the precise user and application associated with a network connection. Through the use of dynamic correlation and graph modeling, we developed a visualization tool called ENAVis (Enterprise Network Activities Visualization). ENAV is aids a real-world administrator in allowing them to more efficiently manage and gain insight about the connectivity between hosts, users, and applications that is otherwise obfuscated, lost or not collected in systems currently deployed in an enterprise setting. |
Year | Venue | Keywords |
|---|---|---|
2008 | LISA | enterprise setting,enterprise network activities visualization,large-scale network,inferring user identity,finer-grained data,challenging task,network connection,application usage,precise user,enterprise network,management system |
Field | DocType | Citations |
Graph,World Wide Web,Computer science,Visualization,Enterprise information system,Obfuscation,Management system,Enterprise private network,Network connection | Conference | 6 |
PageRank | References | Authors |
0.54 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Liao | 1 | 97 | 12.60 |
| Andrew Blaich | 2 | 38 | 4.79 |
| Aaron Striegel | 3 | 321 | 42.30 |
| Douglas Thain | 4 | 1530 | 127.00 |