Threat Detection and Analysis in the Internet of Things using Deep Packet Inspection

Abstract

Artificial Neural Networks are universal and highly flexible function approximators first used in the fields of cognitive science and engineering. In recent years, Neural Networks have become increasingly popular in finance for tasks such as pattern recognition, classification and time series forecasting. The ability to predict cash requirement within reasonable accuracy of actual demand provides target for supply optimization well in time. Every financial institution (large or small) faces the same daily challenge. While it would be devastating to run out of cash, it is important to keep cash at the right levels to meet customer demand. In such case, it becomes very necessary to have a forecasting system in order to get a clear picture of demand well in advance. This paper presents two neural network models for cash forecasting for a bank branch. One is daily model – taking the parameter values for a day as input to forecast cash requirement for the next day and the other is weekly model, which takes the withdrawal affecting input patterns of a week to pred ict cash requirement for the next week. The system performs better than other cash forecasting systems. This system can be scaled for all branches of a bank in an area by incorporating historical data from these branches.