Title: A hybrid deep learning method for URL spoofing in websites

Authors: B.V. Santhosh Krishna; S. Vidhya; S. Krishnaveni; N. Ashokkumar

Addresses: Department Of Computer Science and Engineering, Bangalore Technological Institute, Bengaluru, 560035, Karnataka, India ' Computer Science and Engineering Department, V.S.B. Engineering College, Karur, India ' Department of Information Technology, S.A. Engineering College, Chennai, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Mohan Babu University, Andhra Pradesh, India

Abstract: In the 21st century, website uniform resource locator (URL) faking is still a way that phishing attacks are carried out. Hackers are still using URL faking to trick people who are not paying attention into giving out personal information on harmful websites. An important and well-known deep learning method is the convolutional neural network (CNN). Long-short-term memory (LSTM), on the other hand, has been used well in tough real-time situations because it can keep information for a long time. CNN and LSTM deep learning models are used together to see how well they can find fake website URLs. The goal is to use the best parts of both methods to create a more advanced faking URL detection system. We compared the suggested hybrid model to other models using two datasets. The UCL and PhishTank datasets were used to test the combined CNN-LSTM model, obtaining 98.9% and 96.8% respectively.

Keywords: data collection; convolutional neural network; CNN; long-short-term memory; LSTM.

DOI: 10.1504/IJESDF.2026.156138

International Journal of Electronic Security and Digital Forensics, 2026 Vol.18 No.5, pp.524 - 537

Received: 06 Jul 2024
Accepted: 10 Oct 2024
Published online: 07 Sep 2026 *

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