• Komal Londhe
• Mansi Netke
• Rutuja Ghaskadbi
• Mahesh Bagal

Phishing attacks typically rely on social networking techniques applied to email or other electronic communication methods. Some methods include direct messages sent over social networks and SMS text messages. We have implemented a deep learning-based framework as a browser plug-in capable of determining whether there is a phishing risk in real-time when the user visits a web page and gives a warning message. The real-time prediction service combines multiple strategies to improve accuracy, reduce false alarm rates, and reduce calculation time, including whitelist filtering, blacklist interception, and machine learning (ML) prediction. The browser plug-in receives client information, calls the background prediction service, and shows the prediction results to users. It is a deep learning-based framework for detecting phishing URLs. We trained and tested the models using seven custom datasets generated from four existing data sources, and we achieved the highest accuracy with the RNN-GRU model. It has a prototype implementation of the proposed framework as a Chrome browser extension.

Phishing detection, machine learning, deep learning, RNN-GRU, web browser extension.

"A Literature Review on Phishing Website detection method based on deep learning framework using Recurrent Neural Network –GRU Model ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.m116-m120, May-2023, Available :http://www.jetir.org/papers/JETIR2305C17.pdf

"A Literature Review on Phishing Website detection method based on deep learning framework using Recurrent Neural Network –GRU Model ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppm116-m120, May-2023, Available at : http://www.jetir.org/papers/JETIR2305C17.pdf