A SOFT COMPUTING APPROACH TO DETECT E-BANKING PHISHING WEBSITES USING ARTIFICIAL NEURAL NETWORK

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 indicate 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 classification. Therefore, when a new phishing web page is introduced, the detection algorithms find it difficult to correctly classify it as phishing. This paper puts 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 of accuracy and reduced false positive rate during detection. This shows that, the ANN algorithm with confusion matrix analysis can produce competitive results that is suitable for detecting phishing in e-banking websites.