| Title | Phishing Detection from URLs Using Deep Learning Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Singh, Shweta, Singh, M.P., Pandey, Ramprakash |
| Conference Name | 2020 5th International Conference on Computing, Communication and Security (ICCCS) |
| Date Published | oct |
| Keywords | blacklisting, Browsers, convolution, cyber security, cyber-attack detection, Deep Learning, glove, Human Behavior, Internet, Neural networks, phishing, pubcrawl, Web pages |
| Abstract | Today, the Internet covers worldwide. All over the world, people prefer an E-commerce platform to buy or sell their products. Therefore, cybercrime has become the center of attraction for cyber attackers in cyberspace. Phishing is one such technique where the unidentified structure of the Internet has been used by attackers/criminals that intend to deceive users with the use of the illusory website and emails for obtaining their credentials (like account numbers, passwords, and PINs). Consequently, the identification of a phishing or legitimate web page is a challenging issue due to its semantic structure. In this paper, a phishing detection system is implemented using deep learning techniques to prevent such attacks. The system works on URLs by applying a convolutional neural network (CNN) to detect the phishing webpage. In paper [19] the proposed model has achieved 97.98% accuracy whereas our proposed system achieved accuracy of 98.00% which is better than earlier model. This system doesn't require any feature engineering as the CNN extract features from the URLs automatically through its hidden layers. This is other advantage of the proposed system over earlier reported in [19] as the feature engineering is a very time-consuming task. |
| DOI | 10.1109/ICCCS49678.2020.9277459 |
| Citation Key | singh_phishing_2020 |
Phishing Detection from URLs Using Deep Learning Approach