Multilayer perceptron neural network technique for fraud detection

Abstract

Fraud detection is an enduring topic that pose a threat to banking, insurance, financial sectors and information security systems such as intrusion detection systems (IDS), etc. Data mining and machine learning techniques help to anticipate and quickly detect fraud and take immediate action to minimize costs. This paper starts with the definition of intrusion detection system and its types, focuses on the implementation of a set of well-known machine learning classification algorithms (Decision Trees, Naive Bayes and Artificial Neural Networks), which can reduce the existing disadvantages of the intrusion detection systems. Experimental results on NSL-KDD dataset infer that our ANN-MLP method (Multilayer Perceptron) yields average better performance by calculating “confusion matrix” that in turn helps us to calculate performance measure such as, “Detection Rate Accuracy”, “precision” and “recall”.