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All Journal Journal Serambi Engineering (JSE) JSSIT: Jurnal Sains dan Sains Terapan Journal of ICT, Design, Engineering and Technological Science Jurnal Sains dan Sains Terapan
Mutasim Billah
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics

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Journal : Journal of ICT, Design, Engineering and Technological Science

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A Hybrid NLP and Deep Learning Framework for Phishing Detection in Emails and URLs Basheer Riskhan; Md Saiful Arefin; Mutasim Billah; Abdullah Al Hadi; Siti Shafrah Shahawai; Siva Raja Sindiramutty; Noor Zaman Jhanjhi
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.2.5

Abstract

Phishing attacks are constantly evolving, exploiting users with malicious URLs and misleading emails, while conventional rule‑based detection methods struggle to keep pace with new threats. To improve detection accuracy and adaptability, this study proposes a hybrid phishing detection framework that combines Deep Learning (DL) and Natural Language Processing (NLP) techniques. For email classification, the system uses TF‑IDF‑based feature extraction, including word‑ and character‑level n‑grams, domain encoding, and link‑count analysis; for URL analysis, character‑level tokenisation and manually created structural features are used. In addition to CNN, LSTM, and Hybrid CNN‑LSTM models for URL classification, three deep learning architectures are developed for email detection: Convolutional Neural Network (CNN), Bidi‑rectional Long Short‑Term Memory (BiLSTM), and a Hybrid CNN‑BiLSTM model. The hybrid architectures efficiently capture intricate phishing patterns by combining sequential dependency learning with spatial feature extraction. Both primary email and large‑scale URL datasets are used, with stratified data partitioning and suitable preprocessing methods, to assess the proposed framework. The methodology addresses the drawbacks of static, single‑model systems in contemporary cybersecurity environments by demonstrating a scalable, flexible approach to phishing detection.
Co-Authors Abdullah Al Hadi Adifa Azzajalla Robitu AR Yelvia Sunarti Ardika Nurmawati Basheer Riskhan Cut Difasya Auriely Tamami Elene Afrisia Efendi Erwan Adi Saputro Md Saiful Arefin Noor Zaman Jhanjhi Siti Shafrah Shahawai Siva Raja Sindiramutty