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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Rao, Kongara Srinivasa
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Phishing website detection using novel integration of BERT and XLNet with deep learning sequential models Rao, Kongara Srinivasa; Valluru, Dinesh; Patnala, Satishkumar; Devareddi, Ravi Babu; Rama Krishna, Tummalapalli Siva; Sravani, Andavarapu
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1273-1283

Abstract

Phishing websites pose a significant threat to online security, necessitating robust detection mechanisms to safeguard users' sensitive information. This study explores the efficacy of various deep learning architectures for phishing website detection. Initially, traditional sequential models, including recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), achieve accuracies of 95%, 96%, and 96.5%, respectively, on a curated dataset. Building upon these results, hybrid architectures that combine the strengths of traditional sequential models with state-of-the-art language representation models, bidirectional encoder representations from transformers (BERT) and XLNet, are investigated. Combinations such as RNN with BERT, BERT with LSTM, BERT with GRU, RNN with XLNet, XLNet with LSTM, and XLNet with GRU are evaluated. Through experimentation, accuracies of 94.5%, 96.5%, 96.1%, 95.7%, 97.4%, and 97%, respectively, are achieved, demonstrating the effectiveness of hybrid deep learning architectures in enhancing phishing detection performance. These findings contribute to advancing the state-of-the-art in cybersecurity practices and underscore the importance of leveraging diverse model types to combat online threats effectively.
Co-Authors Devareddi, Ravi Babu Patnala, Satishkumar Rama Krishna, Tummalapalli Siva Sravani, Andavarapu Valluru, Dinesh