Data Mining Approaches for Intrusion Detection
Abstract
In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the sendmail system call data and the network tcpdump data, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intra- and inter- audit record patterns, which are essential in describing program or user behavior.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 12, 2000
- Accession Number
- ADA401496
Entities
People
- Salvatore J. Stolfo
- Wenke Lee
Organizations
- Columbia University