Data mining approaches for intrusion detection

In this paper we discuss our research in developing gen-eral 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 de-scribe program and user behavior, and use the set of rel-evant system features to compute (inductively learned) classifiers that can recognize anomalies and known in-trusions. Using experiments on the sendmail system call data and the network tcpdump data, we demonstrate that we can construct concise and accurate classifiers to de-tect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes al-gorithm. These algorithms can be used to compute the intra- and inter- audit record patterns, which are essential in describing program or user behavior. The discovered patterns can guide the audit data gathering process and facilitate feature selection. To meet the challenges of both efficient learning (mining) and real-time detection, we propose an agent-based architecture for intrusion de-tection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents.