An approach toward user behavior modeling that takes advantage of the properties of neural algorithms is described, and results obtained on preliminary testing of the approach are presented. The basis of the approach is the IDES (Intruder Detection Expert System) which has two components, an expert system looking for evidence of attacks on known vulnerabilities of the system and a statistical model of the behavior of a user on the computer system under surveillance. This model learns the habits a user has when he works with the computer, and raises warnings when the current behavior is not consistent with the previously learned patterns. The authors suggest the time series approach to add broader scope to the model. They therefore feel the need for alternative techniques and introduce the use of a neural network component for modeling user's behavior as a component for the intrusion detection system.>