A hybrid approach to the profile creation and intrusion detection

Anomaly detection involves characterizing the behaviors of individuals or systems and recognizing behavior that is outside the norm. This paper describes some preliminary results concerning the robustness and generalization capabilities of machine learning methods in creating user profiles based on the selection and subsequent classification of command line arguments. We base our method on the belief that legitimate users can be classified into categories based on the percentage of commands they use in a specified period. The hybrid approach we employ begins with the application of expert rules to reduce the dimensionality of the data, followed by an initial clustering of the data and subsequent refinement of the cluster locations using a competitive network called Learning Vector Quantization. Since Learning Vector Quantization is a nearest neighbor classifier, and new record presented to the network that lies outside a specified distance is classified as a masquerader. Thus, this system does not require anomalous records to be included in the training set.