Quickprop Neural Network Ensemble Forecasting Framework for a Database Intrusion Prediction System

This paper describes a framework for a statistical anomaly prediction system using ensemble Quickprop neural network forecasting model, which predicts unauthorized invasions of user based on previous observations and takes further action before intrusion occurs. This paper focuses on detecting significant changes of transaction intensity for intrusion prevention. The experimental study is performed using real data provided by a major Corporate Bank. A comparative evaluation of the two ensemble networks over the individual networks was carried out using mean absolute percentage error on a prediction data set and a better prediction accuracy has been observed. Keywords—Database Security Database Anomaly Intrusion Prediction Quickprop Prediction Technique Intrusion Prevention Artificial Neural Networks – Uncertainty