Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal The Indonesian Journal of Computer Science
Dzati Athiar Ramli
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Detection of Phishing Webpages Using a CNN-BiGRU Hybrid Deep Learning Framework Saima Anwar Lashari; Hadeel Abdulrahman Alsantli; Khan, Abdullah; Dzati Athiar Ramli
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5067

Abstract

Protecting sensitive data, like passwords and financial information in the cyber-world, is becoming a critical challenge day by day. Attackers use various smart ways to exploit the mistakes of internet users. Phishing is one of the most important types of cyber-attack. Researchers have proposed various phishing detection and identification techniques in the last decade against the phishing attacks. However, many state-of-the-art techniques have shortcomings in terms of accuracy and time complexity. But they also have major issues of the high runtime overhead. On the other hand, the simple techniques with low time-complexity have issue of the accuracy because these simple techniques have high false alarm rate. To resolve these issues, this study proposed a novel hybrid-deep-learning algorithm with 3 variants to address these high time-complexity and low accuracy issues. A novel hybrid deep learning model based on Convolutional Neural Network (CNN)-(Bi-GRU) is proposed to classify a web-page phishing or legitimate. To validate the proposed hybrid model with various variants, extensive experiments have been conducted on various benchmark datasets. The experimental results have proved the validity of the proposed model as compared to state-of-the-art techniques in terms of identifying the phishing webpages accurately in comparatively less time.
Co-Authors Abdullah Khan Hadeel Abdulrahman Alsantli Saima Anwar Lashari