| Publisher | Columbia University | ||
|---|---|---|---|
| Format | 71.4KB PDF | Date added | 07 Jun 2000 |
| Topics | Network Security, Security Tools, Intrusion Detection Systems | ||
| Downloads | 39 | ||
This paper presents adaptive model generation, a method for automatically building detection models for data-mining based intrusion detection systems. Using the same data collected by intrusion detection sensors, adaptive model generation builds detection models on the fly. This significantly reduces the deployment cost of an intrusion detection system because it does not require building a training set. The paper presents a real time system architecture and efficient implementation of automatic model generation. The system uses a model building algorithm that builds anomaly detection models over noisy data. The system using the DARPA Intrusion Detection Evaluation data is evaluated and shows an increase in detection performance as more data is collected by the sensors.
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