| Data Mining for Improving Intrusion Detection December 2000
Dr. Eric Bloedorn, The MITRE Corporation
ABSTRACT
In this presentation I'll discuss our experiences in applying data
mining techniques to improving intrusion detection for the MITRE network.
MITRE has a large, distributed network that is hit with approximately
300 incidents per week. These incidents represent an even large number
of raw sensor events that all need to be reviewed by human analysts.
Our work in applying data mining to this task is primarily focused on
reducing this burden on the human analysts. To do this we have worked
on deriving useful features and on understanding how clustering, anomaly
detection and classication can most effectively be used.
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