| Publisher | University of Minnesota | ||
|---|---|---|---|
| Format | 590.1KB PDF | Date added | 19 Nov 2002 |
| Topics | Data Mining - Analysis, Network Security, Intrusion Detection Systems | ||
| Downloads | 161 | ||
This paper gives an overview of the research in building rare class prediction models for identifying known intrusions and their variations and anomaly/outlier detection schemes for detecting novel attacks whose nature is unknown. Experimental results on the KDDCup'99 data set have demonstrated that the rare class predictive models are much more efficient in the detection of intrusive behavior than standard classification techniques. Experimental results on the DARPA 1998 data set, as well as on live network traffic at the University of Minnesota, show that the new techniques show great promise in detecting novel intrusions.
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