A multi-algorithm data mining classification approach for bank fraudulent transactions

Abstract

This paper proposes a multi-algorithm strategy for card fraud detection. Various techniques in data mining have been used to develop fraud detection models; it was however observed that existing works produced outputs with false positives that wrongly classified legitimate transactions as fraudulent in some instances; thereby raising false alarms, mismanaged resources and forfeit customers' trust. This work was therefore designed to develop a hybridized model using an existing technique Density-Based Spatial Clustering of Applications with Noise (DBSCAN) combined with a rule base algorithm to reinforce the accuracy of the existing technique. The DBSCAN algorithm combined with Rule base algorithm gave a better card fraud prediction accuracy over the existing DBSCAN algorithm when used alone.Key words: Card fraud detection, density-based spatial clustering of applications with noise (DBSCAN), rule base algorithm, data mining.

Security Evaluation of Side Channel Attacks on CPS Architectures

“…Estimation of the safety status is presented using an algorithm. Additionally, a set of algorithms developed by Oluwafolake and Solomon [6] may identify fraudulent data opportunists in large-power process model. A drawback of the algorithmic approach described above is that it cannot ensure accuracy, while the observer-based strategy responds quickly and conserves computational resources.…”