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Adaptive Transfer Learning for Robust Phishing Attack Detection Using Recurrent Layers: Enhancing Cybersecurity Through Dynamic Defense Mechanisms
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Author(s): Alnoman Mundher Tayyeh (Institute of Technology, Middle Technical University, Iraq), Abdulsattar Abdullah Hamad (College of Education, University of Samarra, Iraq), Rana B. Yaseen (College of Education for Women, Tikrit University, Iraq), Khalid Saeed Lateef Al-Badri (College of Education, Samarra University, Iraq), Firas Tarik Jasim (Northern Technical University, Al-Dour Technical Institute, Iraq), Noor Mohammed Kadhim (College Education for Human Sciences, Wasit University, Iraq), Shafeeq K. S. Aldoori (University of Samarra, Iraq), Husam Abdulhameed Hussein (University of Samarra, Iraq), Ahmed Ibrahim Turki (University of Samarra, Iraq)and Omar Azeez Abbas (College of Administration and Economics, University of Samarra, Iraq)
Copyright: 2025
Pages: 18
Source title:
Integrating Intelligent Control Systems With Sensor Technologies
Source Author(s)/Editor(s): Abdulsattar Abdullah Hamad (University of Samarra, Iraq), Sudan Jha (Kathmandu University, Nepal)and Khalid Al-Badri (University of Samarra, Iraq)
DOI: 10.4018/979-8-3373-0330-7.ch011
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Abstract
This paper proposes a robust phishing detection framework the use of adaptive switch getting to know mixed with recurrent layers, such as Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs). Phishing assaults pose a significant chance to cybersecurity, and conventional detection techniques have struggled to maintain pace with the dynamic nature of those assaults. The proposed framework leverages the strength of switch getting to know to conform to new phishing styles with out requiring widespread retraining. By integrating recurrent layers, the model captures temporal dependencies inherent in phishing emails and verbal exchange styles, making an allowance for more accurate detection of evolving threats. The framework is designed to enhance cybersecurity by way of dynamically adjusting to new phishing approaches, supplying a scalable and effective solution for phishing detection.
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