Quickprop neural network short-term forecasting framework for a database intrusion prediction system

摘要: This paper describes a framework for statistical anomaly prediction system using Quickprop neural network forecasting model, which predicts unauthorized invasions of user based on previous observations and takes further action before intrusion occurs. The experimental study is performed real data provided by major Corporate Bank. A comparative evaluation the over traditional models was carried out mean absolute percentage error set better accuracy has been observed. Further, in order to make legitimate comparison, dataset divided into two statistically equivalent subsets, viz. training sets, genetic algorithm.