Detection of Phishing Website Based on Deep Learning

Authors

  • Harsha N. Digwal M.Tech. Student, Department of Computer science and Engineering, RNS Institute of Technology, Bangalore, India
  • N. P. Kavya Professor, Department of Computer science and Engineering, RNS Institute of Technology, Bangalore, India

Keywords:

Convolutional Neural Network, Long short term memory, Phishing website, Machine Learning

Abstract

Phishing can be described as a route for someone to try to retrieve some personal and important information such as credentials, passwords and credit / debit card details for the wrong reasons by acting as a trusted authority. Many websites that seem real to us can be phishing and can be targeted by various online scams. This phishing site can try to retrieve important data in various ways, e.g. B. calls, messages and pop-ups. Therefore, it is very important to ensure that data is sent to the network. Fighting this phishing attack is a solid way to do this. To overcome this limitation, we recommend a multi-dimensional phishing detection method based on rapid detection techniques through deep learning. This document focuses on various detailed training algorithms that can be used to predict whether a website is phishing or legitimate. Comprehensive training solutions can detect phishing attacks within an hour and are better suited for working with new types of phishing attacks. Because of this, they are preferred. In our implementation, the data set contains millions of phishing and legitimate URLs, the accuracy reaches 98.99% and the false positive frequency is only 0.59%.

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Published

20-08-2020

Issue

Section

Articles

How to Cite

[1]
H. N. Digwal and N. P. Kavya, “Detection of Phishing Website Based on Deep Learning”, IJRESM, vol. 3, no. 8, pp. 331–336, Aug. 2020, Accessed: Jul. 21, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/192