• RESHMA R
• DR. R. VIJAYAKUMAR
• DR. SINDHU S

Phishing website is an illegitimate websites that is designed by dishonest people to mimic a real website. Those who are entering in such website may expose their sensitive information to the attacker whom might use this information for financial and criminal activities. In this technological world, phishing websites are created using new techniques allows them to escape from most anti-phishing tool. So that, the white list and blacklist based techniques are less effective when compared with the recent phishing trends. Advanced to that, there exists some tools using machine learning and deep learning approaches by examining webpage content in order to detect phishing websites. Along with the rapid growth of phishing technologies it is needed to improve effectiveness and efficiency of phishing website detection. This work reviewed many papers those proposed different real time as well as non real time techniques. As the result this study suggests a Convolutional Neural Network (CNN) framework with 18 layers and scope of transfer learning in AlexNet for the classification of websites using screenshot images and URLs of phishing and legitimate websites . CNN is a class of deep, feed-forward artificial neural networks (where connections between nodes do not form a cycle) & use a variation of multilayer perceptrons designed to require minimal preprocessing.

Phishing, Character level CNN, Deep learning, transfer learning, APWG, AlexNet

"SCOPE OF VISUAL BASED SIMILARITY APPROACH USING CONVOLUTIONAL NEURAL NETWORK ON PHISHING WEBSITE DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.312-319, June 2019, Available :http://www.jetir.org/papers/JETIR1907505.pdf

"SCOPE OF VISUAL BASED SIMILARITY APPROACH USING CONVOLUTIONAL NEURAL NETWORK ON PHISHING WEBSITE DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp312-319, June 2019, Available at : http://www.jetir.org/papers/JETIR1907505.pdf