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 behaviors 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 a partial matching function, biased toward adjacent matches, with a polynomial growth rate is superior for this domain.

The Database Language SQL (SQL) is a standard interface for accessing and manipulating relational databases. AN SQL-compliant database management system (DBMS) will include a minimum level of functionality in a variety of areas.  However, many additional areas are left unspecified by the SQL standard; the functionality will vary according to the particular version. This document examines the security functionality that might be required of relational DBMS\‘s and compares them with the requirements and options of the SQL specifications. THe comparison will show that the security functionality of an SQL compliant DBMS may vary greatly.  A variety of security policies are considered which can be supported by SQL.  The document ends by showing which types of functions are required by the examined security policies.