Sequence Matching and Learning in Anomaly Detection for Computer Security

Two problems of importance in computer security are to 1) detect the presence of an intruder masquerading as the valid user and 2) detect the perpetration of abusive actions on the part of an otherwise innocuous user. We have developed an approach to these problems that examines sequences of user actions (UNIX commands) to classify behavior as normal or anomalous. In this paper we explore the matching function needed to compare a current behavioral sequence to a historical profile. We discuss the difficulties of performing matching in human-generated data and show that exact string matching is insufficient to this domain. We demonstrate a number of partial matching functions and examine their behavior on user command data. In particular, we explore two methods for weighting scores by adjacency of matches as well as two growth functions (polynomial and exponential) for scoring similarities. We find, empirically, that the optimal similarity measure is user dependant but that measures based on the assumption of causal linkage between user commands are superior for this domain.

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