Name
Abstract | ||
|---|---|---|
We focus on detecting insider access violations to off-topic documents. Previously, we utilized information retrieval techniques, e.g., clustering and relevance feedback, to warn of potential misuse. For the relevance feedback approach, we minimize the indicative features needed for detection using data mining techniques. We show that the derived reduced feature subset achieves equivalent performance to that of the previously derived full set of features. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1099554.1099663 | CIKM |
Keywords | DocType | ISBN |
insider access violation,information system,information retrieval technique,full set,feature subset,off-topic access detection,relevance feedback,indicative feature,equivalent performance,potential misuse,relevance feedback approach,data mining technique,data mining,information retrieval,security | Conference | 1-59593-140-6 |
Citations | PageRank | References |
6 | 0.75 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nazli Goharian | 1 | 460 | 49.93 |
| Ling Ma | 2 | 50 | 5.36 |