A study of phishing website detection based on machine learning methods

Zetian Luo; Qingyi Zhang; Yuang Zhang

doi:10.1117/12.2624893

13 December 2021 A study of phishing website detection based on machine learning methods

Zetian Luo, Qingyi Zhang, Yuang Zhang

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 1208711 (2021) https://doi.org/10.1117/12.2624893
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

Phishing attack is the simplest way to obtain sensitive information from innocent users. The target of phishers is to obtain key information, such as username, password, and bank account details. Cyber security officials are now looking for reliable and stable detection techniques to detect phishing sites. This paper studies the phishing websites detection technology by extracting and analyzing the characteristics of legal and forged Uniform Resource Locators (URLs) using machine learning approaches, including Logistic Regression, K-nearest Neighbor (KNN), Linear Support Vector Classifier (SVC), Random Forest, Gradient Boosting Decision Tree, and Ada-Boost, and compares their performance with respect to criterions such as accuracy, Root Mean Square Error (RMSE), precision, recall, and F1-score. The results show that ensemble methods, including Gradient Boosting Decision Tree, Random Forest, and Ada Boosting, can achieve much higher detection performance than the other algorithms in terms of all the criteria.

Citation Download Citation

Zetian Luo, Qingyi Zhang, and Yuang Zhang "A study of phishing website detection based on machine learning methods", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 1208711 (13 December 2021); https://doi.org/10.1117/12.2624893

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