An Episode-based approach to Identify Website user access patterns

Abstract

Mining web access log data is a popular technique to identify frequent access patterns of website users. There are many mining techniques such as clustering, sequential pattern mining and association rule mining to identify these frequent access patterns. Each can find interesting access patterns and group the users, but they cannot identify the slight differences between accesses patterns included in individual clusters. But in reality these could refer to important information about attacks. This paper introduces a methodology to identify these access patterns at a much lower level than what is provided by traditional clustering techniques, such as nearest neighbour based techniques and classification techniques. This technique makes use of the concept of episodes to represent web sessions. These episodes are expressed in the form of regular expressions. To the best of our knowledge, this is the first time to apply the concept of regular expressions to identify user access patterns in web server log data. In addition to identifying frequent patterns, we demonstrate that this technique is able to identify access patterns that occur rarely, which would have been simply treated as noise in traditional clustering mechanisms.