Frequent rule reduction for phishing URL classification using fuzzy deep neural network model

Abstract

Phishing is an online extensive fraud, which can trick Internet clients into uncovering their mystery data and qualifications, e.g., login id, secret key, charge card number, and so on. Phishing is one of the real computer security dangers looked by the digital world and could prompt monetary losses for both industries and people. In this paper, two datasets (Phishtank and UCI) were considered for malicious URL detection analysis. At first, the optimal features are extracted from the datasets using DBA-based detector module and then seventy-five optimal rules are generated using Association Rule mining based on these features. However, it is having some repetitive features which can lessen the efficiency of the malicious URL detection process. To beat this issue, the Frequent Rule Reduction algorithm along with the classification approach for predicting Phishing Websites should be implemented. The outcomes show that the proposed Fuzzy Deep Neural Network classifier accomplishes the most extreme accuracy in the prediction analysis of phishing, non-phishing, and suspicious URL.