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Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Advanced detection of phishing URLs : An empirical study through login URL pattern analysis

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pp. 467–483Vol. 46Issue 2March 2025DOI: 10.47974/JIOS-1928XML
Received:
13 Nov 2024
Published Online:
17 Mar 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1928
Pages:
467–483

Abstract

Phishing attacks pose critical cybersecurity challenges, demanding robust detection methods. This study presents a novel hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for detecting phishing URLs. Leveraging a Kaggle dataset, the research evaluates the performance of CNN, LSTM, and a hybrid CNN-LSTM architecture. The hybrid approach uniquely integrates CNN’s feature extraction capabilities with LSTM’s sequence learning strength, delivering enhanced detection accuracy and reliability. Experimental results underscore its effectiveness in distinguishing phishing and legitimate URLs, providing a significant advancement in phishing detection technologies and addressing the growing need for innovative cyber defence mechanisms. 

Keywords

Primary 93A00Secondary 94A00

Subject Classifications

Phishing detectionConvolutional neural networksLong short-term memoryMachine learning hybrid neural networksURL classification

References

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