Application of Neural Network to Detect Intrusion in Banking System

摘要

One of the major problems in the banking industry is the invincible way in which unauthorized user gain access to her computing resource. There is therefore a growing demand to rapidly detect and identify intrusion in this sector. Thus, this calls for proper monitoring. This paper presents how to detect intrusion in the banking system using an identified method with a proactive approach. An artificial neural network architecture called the Feed Forward Back Propagation will be adopted. The network is trained before tested with the banking intrusion parameters such as the IP address, account number, browser, number of failed attempt login. The result shows paradigm shift from structural pattern recognition techniques to sequential learning techniques. The Neural Network based detection model is able to detect threat or denial of service to intruders in the banking industry.