Evolving Systems for Computer User Behavior Classification

A computer can keep track of computer users to improve the security in the system. However, this does not prevent a user from impersonating another user. Only the user behavior recognition can help to detect masqueraders. Under the UNIX operating system, users type several commands which can be analyzed in order to create user profiles. These profiles identify a specific user or a specific computer user behavior. In addition, a computer user behavior changes over time. If the behavior recognition is done automatically, these changes need to be taken into account. For this reason, we propose in this paper a simple evolving method that is able to keep up to date the computer user behavior profiles. This method is based on Evolving Fuzzy Systems. The approach is evaluated using real data streams.This work has been supported by the Spanish Government under i-Support (Intelligent Agent Based Driver Decision Support) Project (TRA2011-29454-C03-03)

Ensemble method based on individual evolving classifiers

Abstract: Humans often seek a second or third opinion about an important matter. Then, a final decision is reached after weighing and combining these opinions. This idea is the base of the ensemble based systems. Ensembles of classifiers are well established as a method for obtaining highly accurate classifiers by combining less accurate ones. On the other hand, evolving classifiers are inspired by the idea of evolve their structure in order to adapt to the changes of the environment. In this paper, we present a proof-of-concept method for constructing an ensemble system based on Evolving Fuzzy Systems. The main contribution of this approach is that the base-classifiers are self-developing (evolving) Fuzzy-rule-based (FRB) classifiers. Thus, we present an ensemble system which is based on evolving classifiers and keeps the properties of the evolving approach classification of streaming data. It is important to clarify that the evolving classifiers are gradually developing but they are not genetic or evolutionary.This work has been supported by the Spanish Government under i-Support (Intelligent Agent Based Driver Decision Support) Project (TRA2011-29454-C03-03)