Precise Detection of Phishing URLS Using Recurrent Neural Networks

Kamireddy Neeharika*, K. P. Ruphaa Sri **, Vishruthi B. ***, M. Suresh Anand ****
*-**** Department of Computer Science and Engineering, Sri Sairam Engineering College, Chennai, Tamil Nadu, India.
Periodicity:March - May'2021


Nowadays, phishing attacks can be launched from anywhere in the world at insignificant costs by people with little to no technical skills. As the technical skills and costs associated with deploying phishing attacks decline, there is an unprecedented level of scam that is driving the need for more effective methods of proactively detecting phishing threats. In our proposed work, the use of URLs as input has been explored for machine learning models applied for phishing site prediction. In this way, a feature engineering approach has been compared followed by a random forest classifier against a novel method based on recurrent neural networks. The recurrent neural network approach has been determined which provides an accuracy rate even without the need of manual feature creation.


Cyber Security, Phishing, Machine Learning, Website Classification.

How to Cite this Article?

Neeharika, K., Sri, K. P. R., Vishruthi, B., and Anand, M. S. (2021). Precise Detection of Phishing URLS Using Recurrent Neural Networks. i-manager's Journal on Computer Science, 9(1), 21-26.


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