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2026, Vol: 6, Issue: 2
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Research Article
Online Published: 02 Oct 2026
 


A Lightweight Deep Learning Framework for Phishing Detection

Tawadudu Rabiu Idris, Ibrahim Saidu, Sani Muhammad Abdullahi, Sirajo Abdullahi Bakura, Hafsat Omar Mahe.


Abstract
Phishing attacks remain one of the most prevalent cybersecurity threats, exploiting users through deceptive techniques to obtain sensitive information. Existing deep learning approaches such as ResNeXt-GRU have demonstrated high detection accuracy but suffer from computational overhead, limiting their applicability in real-time environments. In addition, their reliance on static hyperparameter optimization techniques limits their ability to adapt to emerging phishing attack patterns, thereby reducing their effectiveness in dynamic real-time environments. In this study, a lightweight deep learning framework for phishing detection is proposed. The model introduces a modified ResNeXt-GRU architecture with pruning and quantization techniques to reduce computational complexity while maintaining accuracy. The proposed framework utilizes URL-based features and deep feature extraction for effective classification. The framework was evaluated through simulation using the PyTorch 2.0+ core libraries with TensorFlow backends to assess its performance. Performance evaluation was conducted using standard metrics including accuracy, precision, recall, F1-score, and execution time. Experimental results show that the proposed model achieved reduced computational cost with higher accuracy, making it suitable for deployment in resource-constrained environments.

Key words: Phishing Detection, Deep Learning, ResNeXt-GRU, Pruning, Quantization


 
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How to Cite this Article
Pubmed Style

Idris TR, Saidu I, Abdullahi SM, Bakura SA, Mahe HO. A Lightweight Deep Learning Framework for Phishing Detection . SJACR. 2026; 6(2): 30-41.

Web Style

Idris TR, Saidu I, Abdullahi SM, Bakura SA, Mahe HO. A Lightweight Deep Learning Framework for Phishing Detection . https://www.sjacrksusta.com/?mno=336371 [Access: October 02, 2026].

AMA (American Medical Association) Style

Idris TR, Saidu I, Abdullahi SM, Bakura SA, Mahe HO. A Lightweight Deep Learning Framework for Phishing Detection . SJACR. 2026; 6(2): 30-41.


Vancouver/ICMJE Style

Idris TR, Saidu I, Abdullahi SM, Bakura SA, Mahe HO. A Lightweight Deep Learning Framework for Phishing Detection . SJACR. (2026), [cited October 02, 2026]; 6(2): 30-41.


Harvard Style

Idris, T. R., Saidu, . I., Abdullahi, . S. M., Bakura, . S. A. & Mahe, . H. O. (2026) A Lightweight Deep Learning Framework for Phishing Detection . SJACR, 6 (2), 30-41.


Turabian Style

Idris, Tawadudu Rabiu, Ibrahim Saidu, Sani Muhammad Abdullahi, Sirajo Abdullahi Bakura, and Hafsat Omar Mahe. 2026. A Lightweight Deep Learning Framework for Phishing Detection . Science Journal of Advanced and Cognitive Research, 6 (2), 30-41.


Chicago Style

Idris, Tawadudu Rabiu, Ibrahim Saidu, Sani Muhammad Abdullahi, Sirajo Abdullahi Bakura, and Hafsat Omar Mahe. "A Lightweight Deep Learning Framework for Phishing Detection ." Science Journal of Advanced and Cognitive Research 6 (2026), 30-41.


MLA (The Modern Language Association) Style

Idris, Tawadudu Rabiu, Ibrahim Saidu, Sani Muhammad Abdullahi, Sirajo Abdullahi Bakura, and Hafsat Omar Mahe. "A Lightweight Deep Learning Framework for Phishing Detection ." Science Journal of Advanced and Cognitive Research 6.2 (2026), 30-41. Print.


APA (American Psychological Association) Style

Idris, T. R., Saidu, . I., Abdullahi, . S. M., Bakura, . S. A. & Mahe, . H. O. (2026) A Lightweight Deep Learning Framework for Phishing Detection . Science Journal of Advanced and Cognitive Research, 6 (2), 30-41.


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