Intelligent multi-agent based back-propagation neural network forecasting model for statistical database anomaly prevention system

This paper describes a framework for highly distributed real-time monitoring approach to database security using Intelligent multi-agents. The statistical anomaly prevention system employs back-propagation neural network forecasting model, which predicts unauthorized invasions of user based on previous observations and takes further action before intrusion occurs. Our back-propagation neural network model makes periodic short-term forecasts, since long-term forecasts cannot accurately predict an intrusion. We use a multivariate time series technique to forecast the hacker's behavior effectively. Our back-propagation neural network model results have been compared with traditional statistical forecasting models, and a better prediction accuracy has been observed. In order to reduce single point of failures in centralized security system, a dynamic distributed system has been designed in which the security management task is distributed across the network using Intelligent multi-agents.