Vote algorithm based probabilistic model for phishing website detection

Abstract

Internet scams have been a major concern for everyone over the past decade. With the advancement of technology, attackers have formulated different kinds of contemporary fraudulent procedures to obtain user’s sensitive information. Phishing is one of the oldest and common fraudulent attempts by which every year millions of internet users fall victim to scams resulting in losing their money. Different techniques and algorithms have been proposed by researchers in detecting phishing websites. However, the detection of phishing websites has few challenges since there are different subjective considerations and ambiguities involved in the detection process. This paper presents a two-stage probabilistic method for detecting phishing websites based on the vote algorithm. In the first stage, 29 different base classifiers have been used and their probabilistic values were calculated. In the second stage, the voting algorithm aggregated the probabilistic values of several base classifiers and the phishing websites were detected using the average of probabilities approach. The voting technique achieved an accuracy of 97.431% outperforming all of the single base classifiers in terms of accuracy.