Feature Classification and Extreme Learning Machine Based Detection of Phishing Websites
Article Sidebar
Main Article Content
Abstract
Phishing is a cyber-attack that uses a phishing website impersonating a real website to deceive internet users into disclosing sensitive information. Attackers using stolen credentials not only utilize them for the targeted website, but they may also be used to access other famous genuine websites. This paper proposes a novel approach for detecting phishing websites using a feature classification technique and an Extreme Learning Machine (ELM) algorithm. The proposed system extracts various features from the website URL and content, including text-based, image-based, and behavior-based features. These features are then classified using a feature selection technique, which selects the most relevant features to improve the detection accuracy. The selected features are then fed into the ELM algorithm, which is a powerful machine learning method for classifying and predicting data. The ELM algorithm It trains upon a huge set of data legitimate & phishing websites, and final outcome model is applied to classify unknown websites as either legitimate or phishing. The proposed approach is evaluated on several benchmark datasets and compared with other state-of-the-art phishing detection methods.
The experimental results demonstrate that the proposed approach achieves high detection accuracy and outperforms other methods in terms of precision, recall, and F1-score. The proposed approach can be used as an effective tool for detecting and preventing phishing attacks, which are a major threat to the security of online users.
Article Details
References
Alhazmi, O., & Sloan, R. (2016). A Survey of Phishing Attacks: Their Types, Detection, and Prevention. Journal of Information Privacy and Security, 12(2), 44-66.
Alam, M. J., & Rahman, M. S. (2019). A Review of Machine Learning Approaches for Phishing Detection. Journal of Cybersecurity and Information Management, 2(1), 1-16.
Mahmood, T., Al-Qershi, O. M., & Al-Fahdawi, S. (2021). Detecting Phishing Websites Based on Feature Selection and Machine Learning Techniques. Journal of Cybersecurity and Information Management, 4(1), 1-14.
Zheng, Y., Wang, Y., Zhang, H., & Xie, L. (2019). A Phishing Website Detection Method Based on Improved Extreme Learning Machine. Journal of Computational Science, 31, 142-152.
J. Gu and H. Xu, (2022) "An Ensemble Method for Phishing Websites Detection Based on XGBoost," 2022 14th International Conference on Computer Research and Development (ICCRD), 2022, pp. 214-219, doi: 10.1109/ICCRD54409.2022.9730579.
Waghmode , S. T. ., & Patil , B. M. . (2023). Adaptive Load Balancing in Cloud Computing Environment. International Journal of Intelligent Systems and Applications in Engineering, 11(1s), 209–217. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2495
L. R. Kalabarige, R. S. Rao, A. Abraham and L. A. Gabralla, (2022) "Multilayer Stacked Ensemble Learning Model to Detect Phishing Websites," in IEEE Access, vol. 10, pp. 79543-79552, 2022, doi: 10.1109/ACCESS.2022.3194672.
C. Pascariu and I. C. Bacivarov, (2021) "Detecting Phishing Websites Through Domain and Content Analysis," 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2021, pp. 1-4, doi: 10.1109/ECAI52376.2021.9515165.
Y. A. Alsariera, V. E. Adeyemo, A. O. Balogun and A. K. Alazzawi, (2020) "AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites," in IEEE Access, vol. 8, pp. 142532-142542, 2020, doi: 10.1109/ACCESS.2020.3013699.
Alshehri, M., & Traore, I. (2021). A new feature extraction method for detecting phishing websites using machine learning. Journal of Computer and System Sciences, 122, 21-33.
Ana Oliveira, Yosef Ben-David, Susan Smit, Elena Popova, Milica Mili?. Machine Learning for Forecasting and Predictive Modeling in Decision Science. Kuwait Journal of Machine Learning, 2(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/199
Bhatt, P., & Patel, J. (2019). Phishing detection using machine learning classifiers: A comparative study. In Proceedings of the International Conference on Intelligent Computing and Control Systems (pp. 1102-1107). IEEE.
Chen, W., Wu, Y., Wang, L., & Li, Z. (2020). A novel phishing website detection method based on decision tree and feature selection. Journal of Ambient Intelligence and Humanized Computing, 11(11), 5089-5099.
Li, M., & Yu, F. (2020). Phishing detection based on deep learning with features fusion. In Proceedings of the 3rd International Conference on Industrial Artificial Intelligence (pp. 51-61). Springer.
Mohammad Hassan, Machine Learning Techniques for Credit Scoring in Financial Institutions , Machine Learning Applications Conference Proceedings, Vol 3 2023.
Wang, J., & Cai, L. (2019). A novel feature selection method based on extreme learning machine for phishing detection. Soft Computing, 23(8), 2739-2749.
Wu, X., & Zhu, J. (2020). A feature selection method based on particle swarm optimization for phishing website detection. Computers & Electrical Engineering, 84, 106650.
Zou, Y., Chen, H., Liu, Y., & Wang, J. (2018). Phishing detection using ensemble extreme learning machine. In Proceedings of the International Conference on Web Information Systems Engineering (pp. 232-240). Springer.