Phishing Website Detection Using Natural Language Processing and Deep Learning Algorithm

Abstract:

Due to rapid growth of the internet most of the people started using internet through mobile and web apps to satisfy their needs. Such as online shopping and banking. Under OWSAP top 10 vulnerabilities, sensitive data exposure is one of the common threats that is identified in recent years and phishing is found to be a key source. Sensitive data exposure is majorly occurring in the internet using various phishing techniques and phishing is found to be a key sources of data stealing. Attackers, not only targeted the financial sectors and e-commerce industries, also in the field of defense and security . To detect the phishing attacks in webpages, many software was used. Some of the method of detection the phishing is, by using the URL of the webpage and by using contents of the webpage. Still, there is no robust and accurate software solution to detect the phishing attacks. The purpose of the research is to use both URL and contents of the webpage to identify the phishing. The proposed work is to build an automated and hybrid model using Random Forest (RF) algorithm in Machine learning with the Convolutional Neural network algorithm (CNN) in Deep Learning is applied to detect and classify the phishing in URL and web page contents in an automated manner .

• Periodical: Advances in Science and Technology (Volume 124)
• Pages: 712-718
• DOI: https://doi.org/10.4028/p-4oja18
• Online since: February 2023
• Authors: M. Thirumaran*, R.P. Karthikeyan, V. Rathaamani
• Keywords: Convolutional Neural Network (CNN), Deep Learning, Machine Learning, Phishing, Random-Forest

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