An agent-based Bayesian forecasting model for enhanced network security

Security has become a major issue in many organisations, but most systems still rely on operating systems, and a user ID and password system to provide user authentication and validation. They also tend to be centralized in their approach which makes them open to an attack. This paper presents a distributed approach to network security using agents, and presents a novel application of the Bayesian forecasting technique to predict user actions. The Bayesian method has been used in the past on weather forecasting and has been expanded so that it can be used to provide enhanced network security by trying to predict user actions. For this a system can determine if a user is acting unpredictably or has changed their normal working pattern. Results are also given which show that the new model can predict user actions, and a set of experiments are proposed for further exploitation of the method.