Open Access

ARTICLE

Modelling an Efficient URL Phishing Detection Approach Based on a Dense Network Model

A. Aldo Tenis^*, R. Santhosh

Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India

* Corresponding Author: A. Aldo Tenis. Email:

Computer Systems Science and Engineering 2023, 47(2), 2625-2641. https://doi.org/10.32604/csse.2023.036626

Received 07 October 2022; Accepted 03 May 2023; Issue published 28 July 2023

Abstract

The social engineering cyber-attack is where culprits mislead the users by getting the login details which provides the information to the evil server called phishing. The deep learning approaches and the machine learning are compared in the proposed system for presenting the methodology that can detect phishing websites via Uniform Resource Locator (URLs) analysis. The legal class is composed of the home pages with no inclusion of login forms in most of the present modern solutions, which deals with the detection of phishing. Contrarily, the URLs in both classes from the login page due, considering the representation of a real case scenario and the demonstration for obtaining the rate of false-positive with the existing approaches during the legal login pages provides the test having URLs. In addition, some model reduces the accuracy rather than training the base model and testing the latest URLs. In addition, a feature analysis is performed on the present phishing domains to identify various approaches to using the phishers in the campaign. A new dataset called the MUPD dataset is used for evaluation. Lastly, a prediction model, the Dense forward-backwards Long Short Term Memory (LSTM) model (), is presented for combining the forward and backward propagation of LSMT to obtain the accuracy of 98.5% on the initiated login URL dataset.

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Keywords

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