How to cite this paper
Al-Tamimi, Y & Shkoukani, M. (2023). Employing cluster-based class decomposition approach to detect phishing websites using machine learning classifiers.International Journal of Data and Network Science, 7(1), 313-328.
Refrences
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Babagoli, M., Aghababa, M. P., & Solouk, V. (2019). Heuristic nonlinear regression strategy for detecting phishing websites. Soft Computing, 23(12), 4315-4327.
Bhardwaj, A., Al-Turjman, F., Sapra, V., Kumar, M., & Stephan, T. (2021). Privacy-aware detection framework to mitigate new-age phishing attacks. Computers & Electrical Engineering, 96, 107546.
Bitaab, M., Cho, H., Oest, A., Zhang, P., Sun, Z., Pourmohamad, R., ... & Ahn, G. J. (2020, November). Scam pandemic: How attackers exploit public fear through phishing. In 2020 APWG Symposium on Electronic Crime Research (eCrime) 1-10.
Chiew, K. L., Tan, C. L., Wong, K., Yong, K. S., & Tiong, W. K. (2019). A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Information Sciences, 484, 153-166.
Deshpande, A., Pedamkar, O., Chaudhary, N., & Borde, S. (2021). Detection of Phishing Websites using Machine Learning, International Journal Of Engineering Research & Technology, 10.
Gautam, S., Rani, K., & Joshi, B. (2018). Detecting phishing websites using rule-based classification algorithm: a comparison. In Information and Communication Technology for Sustainable Development, 21-33.
Grandini, M., Bagli, E., & Visani, G. (2020). Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756.
Gutierrez, C. N., Kim, T., Della Corte, R., Avery, J., Goldwasser, D., Cinque, M., & Bagchi, S. (2018). Learning from the ones that got away: Detecting new forms of phishing attacks. IEEE Transactions on Dependable and Secure Computing, 15(6), 988-1001.
Hannousse, A., & Yahiouche, S. (2021). Towards benchmark datasets for machine learning based website phishing detection: An experimental study. Engineering Applications of Artificial Intelligence, 104, 104347.
Jain, A. K., & Gupta, B. B. (2018). Towards detection of phishing websites on client-side using machine learning based approach. Telecommunication Systems, 68(4), 687-700.
Kamal, G., & Manna, M. (2018). Detection of phishing websites using naïve Bayes algorithms. International Journal of Recent Research and Review, 11(4), 34-38.
Kaytan, M., & Hanbay, D. (2017). Effective classification of phishing web pages based on new rules by using extreme learning machines. Computer Science, 2(1), 15-36.
Koyejo, O. O., Natarajan, N., Ravikumar, P. K., & Dhillon, I. S. (2014). Consistent binary classification with generalized performance metrics. Advances in neural information processing systems, 27.
Liu, C., Wang, L., Lang, B., & Zhou, Y. (2018, January). Finding effective classifier for malicious URL detection. In Proceedings of the 2018 2nd International Conference on Management Engineering, Software Engineering and Service Sciences, 240-244.
McAnulty, B. L. (2021). Phishing Attacks: A Plan to Educate Employees and Mitigate Risks (Doctoral dissertation, Utica College).
Mohammad, R. M., Thabtah, F., & McCluskey, L. (2014). Predicting phishing websites based on self-structuring neural network. Neural Computing and Applications, 25(2), 443-458.
Niakanlahiji, A., Chu, B. T., & Al-Shaer, E. (2018, November). Phishmon: A machine learning framework for detecting phishing webpages. In 2018 IEEE International Conference on Intelligence and Security Informatics, 220-225.
Niu, W., Zhang, X., Yang, G., Ma, Z., & Zhuo, Z. (2017, December). Phishing emails detection using CS-SVM. In 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), 1054-1059.
Pillai, I., Fumera, G., & Roli, F. (2017). Designing multi-label classifiers that maximize F measures: State of the art. Pattern Recognition, 61, 394-404.
Rana, V. K., & Suryanarayana, T. M. V. (2020). Performance evaluation of MLE, RF and SVM classification algorithms for watershed scale land use/land cover mapping using sentinel 2 bands. Remote Sensing Applications: Society and Environment, 19, 100351.
Rao, R. S., Vaishnavi, T., & Pais, A. R. (2019). PhishDump: A multi-model ensemble based technique for the detection of phishing sites in mobile devices. Pervasive and Mobile Computing, 60, 101084.
Sagala, N. T. (2022, January). Comparative Analysis of Grid-based Decision Tree and Support Vector Machine for Crime Category Prediction. In 2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), 184-188.
Shirazi, H., Haefner, K., & Ray, I. (2017, August). Fresh-phish: A framework for auto-detection of phishing websites. In 2017 IEEE international conference on information reuse and integration (IRI), 137-143.
Singh, P., Maravi, Y. P., & Sharma, S. (2015, February). Phishing websites detection through supervised learning networks. In 2015 international conference on computing and communications technologies (ICCCT), 61-65.
Somesha, M., Pais, A. R., Rao, R. S., & Rathour, V. S. (2020). Efficient deep learning techniques for the detection of phishing websites. Sādhanā, 45(1), 1-18.
Subasi, A., & Kremic, E. (2020). Comparison of adaboost with multiboosting for phishing website detection. Procedia Computer Science, 168, 272-278.
Tang, L., & Mahmoud, Q. H. (2021). A survey of machine learning-based solutions for phishing website detection. Machine Learning and Knowledge Extraction, 3(3), 672-694.
Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics.
Tyagi, I., Shad, J., Sharma, S., Gaur, S., & Kaur, G. (2018, February). A novel machine learning approach to detect phishing websites. In 2018 5th International conference on signal processing and integrated networks (SPIN), 425-430.
Ubing, A. A., Jasmi, S. K. B., Abdullah, A., Jhanjhi, N. Z., & Supramaniam, M. (2019). Phishing website detection: An improved accuracy through feature selection and ensemble learning. International Journal of Advanced Computer Science and Applications, 10(1).
Verma, S., & Gautam, A. K. (2020). A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms.
Yadollahi, M. M., Shoeleh, F., Serkani, E., Madani, A., & Gharaee, H. (2019, April). An adaptive machine learning based approach for phishing detection using hybrid features. In 2019 5th International Conference on Web Research (ICWR), 281-286.
Yang, L., Zhang, J., Wang, X., Li, Z., Li, Z., & He, Y. (2021). An improved ELM-based and data preprocessing integrated approach for phishing detection considering comprehensive features. Expert Systems with Applications, 165, 113863.
Yang, P., Zhao, G., & Zeng, P. (2019). Phishing website detection based on multidimensional features driven by deep learning. IEEE access, 7, 15196-15209.
Zhu, E., Ju, Y., Chen, Z., Liu, F., & Fang, X. (2020). DTOF-ANN: an artificial neural network phishing detection model based on decision tree and optimal features. Applied Soft Computing, 95, 106505.
Babagoli, M., Aghababa, M. P., & Solouk, V. (2019). Heuristic nonlinear regression strategy for detecting phishing websites. Soft Computing, 23(12), 4315-4327.
Bhardwaj, A., Al-Turjman, F., Sapra, V., Kumar, M., & Stephan, T. (2021). Privacy-aware detection framework to mitigate new-age phishing attacks. Computers & Electrical Engineering, 96, 107546.
Bitaab, M., Cho, H., Oest, A., Zhang, P., Sun, Z., Pourmohamad, R., ... & Ahn, G. J. (2020, November). Scam pandemic: How attackers exploit public fear through phishing. In 2020 APWG Symposium on Electronic Crime Research (eCrime) 1-10.
Chiew, K. L., Tan, C. L., Wong, K., Yong, K. S., & Tiong, W. K. (2019). A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Information Sciences, 484, 153-166.
Deshpande, A., Pedamkar, O., Chaudhary, N., & Borde, S. (2021). Detection of Phishing Websites using Machine Learning, International Journal Of Engineering Research & Technology, 10.
Gautam, S., Rani, K., & Joshi, B. (2018). Detecting phishing websites using rule-based classification algorithm: a comparison. In Information and Communication Technology for Sustainable Development, 21-33.
Grandini, M., Bagli, E., & Visani, G. (2020). Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756.
Gutierrez, C. N., Kim, T., Della Corte, R., Avery, J., Goldwasser, D., Cinque, M., & Bagchi, S. (2018). Learning from the ones that got away: Detecting new forms of phishing attacks. IEEE Transactions on Dependable and Secure Computing, 15(6), 988-1001.
Hannousse, A., & Yahiouche, S. (2021). Towards benchmark datasets for machine learning based website phishing detection: An experimental study. Engineering Applications of Artificial Intelligence, 104, 104347.
Jain, A. K., & Gupta, B. B. (2018). Towards detection of phishing websites on client-side using machine learning based approach. Telecommunication Systems, 68(4), 687-700.
Kamal, G., & Manna, M. (2018). Detection of phishing websites using naïve Bayes algorithms. International Journal of Recent Research and Review, 11(4), 34-38.
Kaytan, M., & Hanbay, D. (2017). Effective classification of phishing web pages based on new rules by using extreme learning machines. Computer Science, 2(1), 15-36.
Koyejo, O. O., Natarajan, N., Ravikumar, P. K., & Dhillon, I. S. (2014). Consistent binary classification with generalized performance metrics. Advances in neural information processing systems, 27.
Liu, C., Wang, L., Lang, B., & Zhou, Y. (2018, January). Finding effective classifier for malicious URL detection. In Proceedings of the 2018 2nd International Conference on Management Engineering, Software Engineering and Service Sciences, 240-244.
McAnulty, B. L. (2021). Phishing Attacks: A Plan to Educate Employees and Mitigate Risks (Doctoral dissertation, Utica College).
Mohammad, R. M., Thabtah, F., & McCluskey, L. (2014). Predicting phishing websites based on self-structuring neural network. Neural Computing and Applications, 25(2), 443-458.
Niakanlahiji, A., Chu, B. T., & Al-Shaer, E. (2018, November). Phishmon: A machine learning framework for detecting phishing webpages. In 2018 IEEE International Conference on Intelligence and Security Informatics, 220-225.
Niu, W., Zhang, X., Yang, G., Ma, Z., & Zhuo, Z. (2017, December). Phishing emails detection using CS-SVM. In 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), 1054-1059.
Pillai, I., Fumera, G., & Roli, F. (2017). Designing multi-label classifiers that maximize F measures: State of the art. Pattern Recognition, 61, 394-404.
Rana, V. K., & Suryanarayana, T. M. V. (2020). Performance evaluation of MLE, RF and SVM classification algorithms for watershed scale land use/land cover mapping using sentinel 2 bands. Remote Sensing Applications: Society and Environment, 19, 100351.
Rao, R. S., Vaishnavi, T., & Pais, A. R. (2019). PhishDump: A multi-model ensemble based technique for the detection of phishing sites in mobile devices. Pervasive and Mobile Computing, 60, 101084.
Sagala, N. T. (2022, January). Comparative Analysis of Grid-based Decision Tree and Support Vector Machine for Crime Category Prediction. In 2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), 184-188.
Shirazi, H., Haefner, K., & Ray, I. (2017, August). Fresh-phish: A framework for auto-detection of phishing websites. In 2017 IEEE international conference on information reuse and integration (IRI), 137-143.
Singh, P., Maravi, Y. P., & Sharma, S. (2015, February). Phishing websites detection through supervised learning networks. In 2015 international conference on computing and communications technologies (ICCCT), 61-65.
Somesha, M., Pais, A. R., Rao, R. S., & Rathour, V. S. (2020). Efficient deep learning techniques for the detection of phishing websites. Sādhanā, 45(1), 1-18.
Subasi, A., & Kremic, E. (2020). Comparison of adaboost with multiboosting for phishing website detection. Procedia Computer Science, 168, 272-278.
Tang, L., & Mahmoud, Q. H. (2021). A survey of machine learning-based solutions for phishing website detection. Machine Learning and Knowledge Extraction, 3(3), 672-694.
Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics.
Tyagi, I., Shad, J., Sharma, S., Gaur, S., & Kaur, G. (2018, February). A novel machine learning approach to detect phishing websites. In 2018 5th International conference on signal processing and integrated networks (SPIN), 425-430.
Ubing, A. A., Jasmi, S. K. B., Abdullah, A., Jhanjhi, N. Z., & Supramaniam, M. (2019). Phishing website detection: An improved accuracy through feature selection and ensemble learning. International Journal of Advanced Computer Science and Applications, 10(1).
Verma, S., & Gautam, A. K. (2020). A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms.
Yadollahi, M. M., Shoeleh, F., Serkani, E., Madani, A., & Gharaee, H. (2019, April). An adaptive machine learning based approach for phishing detection using hybrid features. In 2019 5th International Conference on Web Research (ICWR), 281-286.
Yang, L., Zhang, J., Wang, X., Li, Z., Li, Z., & He, Y. (2021). An improved ELM-based and data preprocessing integrated approach for phishing detection considering comprehensive features. Expert Systems with Applications, 165, 113863.
Yang, P., Zhao, G., & Zeng, P. (2019). Phishing website detection based on multidimensional features driven by deep learning. IEEE access, 7, 15196-15209.
Zhu, E., Ju, Y., Chen, Z., Liu, F., & Fang, X. (2020). DTOF-ANN: an artificial neural network phishing detection model based on decision tree and optimal features. Applied Soft Computing, 95, 106505.