Smart Phishing Detection in Web Pages using Supervised Deep Learning Classification and Optimization Technique ADAM

Abstract

Phishing is the process of portraying malignant web pages in the place of genuine web pages to obtain important and delicate information from the end-user. Nowadays phishing is considered as one of the most serious threats to web security. Most of the existing techniques for phishing detection use Bayesian classification for differentiating malignant web pages from genuine web pages. These methods work well if a dataset contains less no of web pages and they provide accuracy up to 90 percent. In recent years the size of the web is increasing tremendously and the existing methods have not provideda good enough accuracy for large datasets. So this paper proffers an innovative approach to identify phishing websites using hyperlinks available in the source code of the HTML page in the corresponding website. The proposed method uses a feature vector with 30 parameters to detect malignant web pages. These features are used in training the supervised Deep Neural Network model with Adam optimizer for differentiating fraudulent websites from genuine websites. The proposed deep learning model with Adam Optimizer uses a Listwise approach to classify phishing websites and genuine websites. The performance of the proposed approach is decent when compared to other traditional machine learning approaches like SVM, Adaboost, AdaRank. The results show that the proposed approach provides more accurate results in detecting phishing websites.