How to cite this paper
Alsharaiah, M., Amin, M., Alqutaish, A & Alradwan, G. (2026). Phishing website detection model based on Tabular Multi-Head Attention (Tabmha).International Journal of Data and Network Science, 10(2), 567-576.
References
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Addula, S. R., Norozpour, S., & Amin, M. (2025). Risk Assessment for Identifying Threats, vulnerabilities and countermeasures in Cloud Computing. Jordanian Journal of Informatics and Computing, 2025(1), 38–48. https://doi.org/10.63180/jjic.thestap.2025.1.5
Alamgir, N. (2020). Computer vision-based smoke and fire detection for outdoor environments (Doctoral dissertation, Queensland University of Technology).
Alkhalil, Z., Hewage, C., Nawaf, L., & Khan, I. (2021). Phishing attacks: A recent comprehensive study and a new anatomy. Frontiers in Computer Science, 3, 563060.
Alsharaiah, M. A., Abu-Shareha, A. A., Abualhaj, M., Baniata, L. H., Adwan, O., Al-Saaidah, A., & Oraiqat, M. (2023). A new phishing website detection framework using ensemble classification and clustering. International Journal of Data and Network Science, 7, 857–864.
Alshingiti, Z., Alaqel, R., Al-Muhtadi, J., Haq, Q. E. U., Saleem, K., & Faheem, M. H. (2023). A deep learning-based phishing detection system using CNN, LSTM, and LSTM-CNN. Electronics, 12(1), 232.
Alzoubi, H. M., & Ghazal, T. M. (2022). The effect of e-payment and online shopping on sales growth: Evidence from the banking industry. International Journal of Data and Network Science, 6(4), 1369–1380.
Asiri, S., Xiao, Y., Alzahrani, S., Li, S., & Li, T. (2023). A survey of intelligent detection designs of HTML URL phishing attacks. IEEE Access, 11, 6421–6443.
Aslan, Ö., Aktuğ, S. S., Özkan-Okay, M., Yılmaz, A. A., & Akın, E. (2023). A comprehensive review of cybersecurity vulnerabilities, threats, attacks, and solutions. Electronics, 12(6), 1333.
Bawany, N. Z., Shamsi, J. A., & Salah, K. (2017). DDoS attack detection and mitigation using SDN: methods, practices, and solutions. Arabian Journal for Science and Engineering, 42(2), 425-441.
Fadheel, W., Abusharkh, M., & Abdel-Qader, I. (2017). On feature selection for the prediction of phishing websites. In IEEE 15th International Conference on Dependable, Autonomic and Secure Computing (pp. 1–6).
Ferdous, J., Islam, R., Mahboubi, A., & Islam, M. Z. (2025). A Survey on ML Techniques for Multi-Platform Malware Detection: Securing PC, Mobile Devices, IoT, and Cloud Environments. Sensors (Basel, Switzerland), 25(4), 1153.
Foody, G. M. (2023). Challenges in the real-world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient. PLOS ONE, 18(10), e0291908.
Gupta, D., Gandotra, E., Mohan, Y., & Singh, S. (2023). Analysis of ensemble methods for phishing detection. In Intelligent Multimedia Signal Processing for Smart Ecosystems (pp. 85–100). Springer.
Kalla, D., Samaah, F., Kuraku, S., & Smith, N. (2023). Phishing detection implementation using Databricks and artificial intelligence. International Journal of Computer Applications, 185(11), 1–11.
Kashkool, H. J. M., Farhan, H. M., Naseri, R. A. S., & Kurnaz, S. (2024). Enhancing facial recognition accuracy and efficiency through integrated CNN, PCA, and SVM techniques. In International Conference on Forthcoming Networks and Sustainability (pp. 57–70).
Khaleel, Y. L., Habeeb, M. A., Albahri, A. S., Al-Quraishi, T., Albahri, O. S., & Alamoodi, A. H. (2024). Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods. Journal of Intelligent Systems, 33(1), 20240153.
Krasnodębska, K., Goch, W., Uhl, J. H., Verstegen, J. A., & Pesaresi, M. (2025). Advancing precision, recall, F-score, and Jaccard index: An approach for continuous, ratio-scale measurements. Environmental Modelling & Software, 193, 106614.
Lertampaiporn, S., Thammarongtham, C., Nukoolkit, C., Kaewkamnerdpong, B., & Ruengjitchatchawalya, M. (2013). Heterogeneous ensemble approach with discriminative features and modified-SMOTEbagging for pre-miRNA classification. Nucleic acids research, 41(1), e21-e21.
Li, Z. (2022). Extracting spatial effects from machine learning models using local interpretation methods: An example of SHAP and XGBoost. Computers, Environment and Urban Systems, 96, 101845.
Lim, K., Park, J., & Kim, D. (2024). Phishing vs. legit: Comparative analysis of client-side resources of phishing and target brand websites. In Proceedings of the ACM Web Conference (pp. 1756–1767).
Majgave, A. B., & Gavankar, N. L. (2024). Automatic phishing website detection and prevention model using transformer deep belief network. Computers & Security, 147, 104071.
Owa, K., & Adewole, O. (2025). Benchmarking machine learning techniques for phishing detection and secure URL classification. International Journal of Computer Science and Mobile Computing, 14(1), 20–37.
Pathak, P., & Shrivas, A. K. (2024). Development of a proposed model using random forest with optimization technique for classification of phishing websites. SN Computer Science, 5(8), 1059.
Prabhakaran, M. K., Chandrasekar, A. D., & Meenakshi Sundaram, P. (2025). PHISH_ATTENTION: Achieving robust phishing website detection with balanced datasets and advanced URL features. The Computer Journal.
Rao, R. S., Pais, A. R., & Anand, P. (2023). A heuristic technique to detect phishing websites using TWSVM classifier. Neural Computing and Applications, 33(11), 5733–5752.
Ren, J., Guo, Y., Zhang, D., Liu, Q., & Zhang, Y. (2018). Distributed and efficient object detection in edge computing: Challenges and solutions. IEEE Network, 32(6), 137-143.
St-Aubin, P., & Agard, B. (2022). Precision and reliability of forecast performance metrics. Forecasting, 4(4), 882–903.
Sykes, B., Simon, L., & Rabin, J. (2024). Unifying and extending precision–recall metrics for assessing generative models. arXiv preprint arXiv:2405.01611.
Tang, L., & Mahmoud, Q. H. (2021). A deep learning-based framework for phishing website detection. IEEE Access, 10, 1509–1521.
Ubing, A. A., Jasmi, S. K. B., Abdullah, A., Jhanjhi, N. Z., & Supramaniam, M. (2019). Phishing website detection: Improved accuracy through feature selection and ensemble learning. International Journal of Advanced Computer Science and Applications, 10(1).
Vacalares, S. T., Ana, B. P. E. S., Dranto, D. Q., & Gallano, J. S. (2024). Bank emails: The language of legit and scam. International Journal of Research and Review.
Vaghela, R. S. (2023). Exploring feature importance in phishing URL detection models. Journal of Cyber Security and Digital Forensics, 2(2), 27–34.
Wang, Y., Ma, W., Xu, H., Liu, Y., & Yin, P. (2023). A lightweight multi-view learning approach for phishing attack detection using transformer with mixture of experts. Applied Sciences, 13(13), 7429.
Weng, J., Jia, X., & Liu, Y. (2023). Study on deep learning-based natural scene text recognition. Academic Journal of Computing & Information Science, 6(2), 44–52.
Xiao, X., Zhang, D., Hu, G., Jiang, Y., & Xia, S. (2020). CNN–MHSA: A Convolutional Neural Network and multi-head self-attention combined approach for detecting phishing websites. Neural Networks, 125, 303-312.
Yusuf, M., Kana, A. F. D., Bagiwa, M. A., & Abdullahi, M. (2024). Multi-classification of breast cancer histopathological images using enhanced shallow convolutional neural network. Journal of Engineering and Applied Science, 72(1), 24.
Zakaria, N. H., Mansor, N. S., Husni, H., & Mohammed, F. (2024). Computing and Informatics.
Zuraiq, A. A., & Alkasassbeh, M. (2019). Phishing detection approaches. In 2019 2nd International Conference on New Trends in Computing Sciences (ICTCS) (pp. 1–6). IEEE.
Abu Zuraiq, A. M., & Alkasassbeh, M. (2019). Phishing detection based on machine learning and feature selection methods. International Journal of Interactive Mobile Technologies, 13(12).
Addula, S. R., Norozpour, S., & Amin, M. (2025). Risk Assessment for Identifying Threats, vulnerabilities and countermeasures in Cloud Computing. Jordanian Journal of Informatics and Computing, 2025(1), 38–48. https://doi.org/10.63180/jjic.thestap.2025.1.5
Alamgir, N. (2020). Computer vision-based smoke and fire detection for outdoor environments (Doctoral dissertation, Queensland University of Technology).
Alkhalil, Z., Hewage, C., Nawaf, L., & Khan, I. (2021). Phishing attacks: A recent comprehensive study and a new anatomy. Frontiers in Computer Science, 3, 563060.
Alsharaiah, M. A., Abu-Shareha, A. A., Abualhaj, M., Baniata, L. H., Adwan, O., Al-Saaidah, A., & Oraiqat, M. (2023). A new phishing website detection framework using ensemble classification and clustering. International Journal of Data and Network Science, 7, 857–864.
Alshingiti, Z., Alaqel, R., Al-Muhtadi, J., Haq, Q. E. U., Saleem, K., & Faheem, M. H. (2023). A deep learning-based phishing detection system using CNN, LSTM, and LSTM-CNN. Electronics, 12(1), 232.
Alzoubi, H. M., & Ghazal, T. M. (2022). The effect of e-payment and online shopping on sales growth: Evidence from the banking industry. International Journal of Data and Network Science, 6(4), 1369–1380.
Asiri, S., Xiao, Y., Alzahrani, S., Li, S., & Li, T. (2023). A survey of intelligent detection designs of HTML URL phishing attacks. IEEE Access, 11, 6421–6443.
Aslan, Ö., Aktuğ, S. S., Özkan-Okay, M., Yılmaz, A. A., & Akın, E. (2023). A comprehensive review of cybersecurity vulnerabilities, threats, attacks, and solutions. Electronics, 12(6), 1333.
Bawany, N. Z., Shamsi, J. A., & Salah, K. (2017). DDoS attack detection and mitigation using SDN: methods, practices, and solutions. Arabian Journal for Science and Engineering, 42(2), 425-441.
Fadheel, W., Abusharkh, M., & Abdel-Qader, I. (2017). On feature selection for the prediction of phishing websites. In IEEE 15th International Conference on Dependable, Autonomic and Secure Computing (pp. 1–6).
Ferdous, J., Islam, R., Mahboubi, A., & Islam, M. Z. (2025). A Survey on ML Techniques for Multi-Platform Malware Detection: Securing PC, Mobile Devices, IoT, and Cloud Environments. Sensors (Basel, Switzerland), 25(4), 1153.
Foody, G. M. (2023). Challenges in the real-world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient. PLOS ONE, 18(10), e0291908.
Gupta, D., Gandotra, E., Mohan, Y., & Singh, S. (2023). Analysis of ensemble methods for phishing detection. In Intelligent Multimedia Signal Processing for Smart Ecosystems (pp. 85–100). Springer.
Kalla, D., Samaah, F., Kuraku, S., & Smith, N. (2023). Phishing detection implementation using Databricks and artificial intelligence. International Journal of Computer Applications, 185(11), 1–11.
Kashkool, H. J. M., Farhan, H. M., Naseri, R. A. S., & Kurnaz, S. (2024). Enhancing facial recognition accuracy and efficiency through integrated CNN, PCA, and SVM techniques. In International Conference on Forthcoming Networks and Sustainability (pp. 57–70).
Khaleel, Y. L., Habeeb, M. A., Albahri, A. S., Al-Quraishi, T., Albahri, O. S., & Alamoodi, A. H. (2024). Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods. Journal of Intelligent Systems, 33(1), 20240153.
Krasnodębska, K., Goch, W., Uhl, J. H., Verstegen, J. A., & Pesaresi, M. (2025). Advancing precision, recall, F-score, and Jaccard index: An approach for continuous, ratio-scale measurements. Environmental Modelling & Software, 193, 106614.
Lertampaiporn, S., Thammarongtham, C., Nukoolkit, C., Kaewkamnerdpong, B., & Ruengjitchatchawalya, M. (2013). Heterogeneous ensemble approach with discriminative features and modified-SMOTEbagging for pre-miRNA classification. Nucleic acids research, 41(1), e21-e21.
Li, Z. (2022). Extracting spatial effects from machine learning models using local interpretation methods: An example of SHAP and XGBoost. Computers, Environment and Urban Systems, 96, 101845.
Lim, K., Park, J., & Kim, D. (2024). Phishing vs. legit: Comparative analysis of client-side resources of phishing and target brand websites. In Proceedings of the ACM Web Conference (pp. 1756–1767).
Majgave, A. B., & Gavankar, N. L. (2024). Automatic phishing website detection and prevention model using transformer deep belief network. Computers & Security, 147, 104071.
Owa, K., & Adewole, O. (2025). Benchmarking machine learning techniques for phishing detection and secure URL classification. International Journal of Computer Science and Mobile Computing, 14(1), 20–37.
Pathak, P., & Shrivas, A. K. (2024). Development of a proposed model using random forest with optimization technique for classification of phishing websites. SN Computer Science, 5(8), 1059.
Prabhakaran, M. K., Chandrasekar, A. D., & Meenakshi Sundaram, P. (2025). PHISH_ATTENTION: Achieving robust phishing website detection with balanced datasets and advanced URL features. The Computer Journal.
Rao, R. S., Pais, A. R., & Anand, P. (2023). A heuristic technique to detect phishing websites using TWSVM classifier. Neural Computing and Applications, 33(11), 5733–5752.
Ren, J., Guo, Y., Zhang, D., Liu, Q., & Zhang, Y. (2018). Distributed and efficient object detection in edge computing: Challenges and solutions. IEEE Network, 32(6), 137-143.
St-Aubin, P., & Agard, B. (2022). Precision and reliability of forecast performance metrics. Forecasting, 4(4), 882–903.
Sykes, B., Simon, L., & Rabin, J. (2024). Unifying and extending precision–recall metrics for assessing generative models. arXiv preprint arXiv:2405.01611.
Tang, L., & Mahmoud, Q. H. (2021). A deep learning-based framework for phishing website detection. IEEE Access, 10, 1509–1521.
Ubing, A. A., Jasmi, S. K. B., Abdullah, A., Jhanjhi, N. Z., & Supramaniam, M. (2019). Phishing website detection: Improved accuracy through feature selection and ensemble learning. International Journal of Advanced Computer Science and Applications, 10(1).
Vacalares, S. T., Ana, B. P. E. S., Dranto, D. Q., & Gallano, J. S. (2024). Bank emails: The language of legit and scam. International Journal of Research and Review.
Vaghela, R. S. (2023). Exploring feature importance in phishing URL detection models. Journal of Cyber Security and Digital Forensics, 2(2), 27–34.
Wang, Y., Ma, W., Xu, H., Liu, Y., & Yin, P. (2023). A lightweight multi-view learning approach for phishing attack detection using transformer with mixture of experts. Applied Sciences, 13(13), 7429.
Weng, J., Jia, X., & Liu, Y. (2023). Study on deep learning-based natural scene text recognition. Academic Journal of Computing & Information Science, 6(2), 44–52.
Xiao, X., Zhang, D., Hu, G., Jiang, Y., & Xia, S. (2020). CNN–MHSA: A Convolutional Neural Network and multi-head self-attention combined approach for detecting phishing websites. Neural Networks, 125, 303-312.
Yusuf, M., Kana, A. F. D., Bagiwa, M. A., & Abdullahi, M. (2024). Multi-classification of breast cancer histopathological images using enhanced shallow convolutional neural network. Journal of Engineering and Applied Science, 72(1), 24.
Zakaria, N. H., Mansor, N. S., Husni, H., & Mohammed, F. (2024). Computing and Informatics.
Zuraiq, A. A., & Alkasassbeh, M. (2019). Phishing detection approaches. In 2019 2nd International Conference on New Trends in Computing Sciences (ICTCS) (pp. 1–6). IEEE.