A Soft Computing Approach to Detecting E-Banking Phishing Websites using Artificial Neural Network with Confusion Matrix

Abstract

Phishing is a cybercrime that is described as an art of cloning a web page of a legitimate company with the aim of obtaining confidential data of unsuspecting internet users. Recent researches indicates that a number of phishing detection algorithms have been introduced into the cyber space, however, most of them depend on an existing blacklist or whitelist for classification. Therefore, when a new phishing web page is introduced, the detection algorithms find it difficult to correctly classifies it as phishy. In this paper, we put forward a soft computing approach called Artificial Neural Network (ANN) algorithm with confusion matrix analysis for the detection of e-banking phishing websites. The proposed ANN algorithm produces a remarkable percentage accuracy and reduced false positive rate during detection. This shows that, the ANN algorithm with confusion matrix analysis can produce a competitive results that is suitable for detecting phishing in e-banking websites.