| Title | Machine Learning Algorithms Evaluation for Phishing URLs Classification |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | BOUIJIJ, Habiba, BERQIA, Amine |
| Conference Name | 2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT) |
| Date Published | dec |
| Keywords | accuracy metric, cyberattack, cybersecurity, feature extraction, features, Human Behavior, Lexical Analysis, machine learning, machine learning algorithms, Measurement, phishing, Phishing-URL, pubcrawl, Radio frequency, Support vector machines, Uniform resource locators |
| Abstract | Phishing URL is a type of cyberattack, based on falsified URLs. The number of phishing URL attacks continues to increase despite cybersecurity efforts. According to the Anti-Phishing Working Group (APWG), the number of phishing websites observed in 2020 is 1 520 832, doubling over the course of a year. Various algorithms, techniques and methods can be used to build models for phishing URL detection and classification. From our reading, we observed that Machine Learning (ML) is one of the recent approaches used to detect and classify phishing URL in an efficient and proactive way. In this paper, we evaluate eleven of the most adopted ML algorithms such as Decision Tree (DT), Nearest Neighbours (KNN), Gradient Boosting (GB), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Support Vector Machines (SVM), Neural Network (NN), Ex-tra\_Tree (ET), Ada\_Boost (AB) and Bagging (B). To do that, we compute detection accuracy metric for each algorithm and we use lexical analysis to extract the URL features. |
| DOI | 10.1109/ISAECT53699.2021.9668489 |
| Citation Key | bouijij_machine_2021 |
Machine Learning Algorithms Evaluation for Phishing URLs Classification