Phishing URL classification using Extra-Tree and DNN

Titre	Phishing URL classification using Extra-Tree and DNN
Publication Type	Conference Paper
Year of Publication	2022
Authors	Bouijij, H, Berqia, A, Saliah-Hassan, H
Conference Name	10th International Symposium on Digital Forensics and Security, ISDFS 2022
Mots-clés	Computer crime, Cyber security, Cybercriminals, Cybersecurity, Deep learning, Deep neural networks, Evaluation metrics, Extra-trees, Forestry, Learning systems, Lexical analysis, Machine-learning, Phishing, Tiny URL, Trees (mathematics), URL, Websites
Abstract	Machine Learning (ML) and Deep Learning (DL) methods have become indispensable in cybersecurity. Recently, they are often used to detect and classify phishing websites. Phishing websites are a major problem that has a negative impact on organization and of societies. Statistics report that the number of phishing website is continuously increasing and it is becoming more difficult to detect them. Various works have shown that ML and DL can be efficient to solve this problem. In this work, we adopted lexical analysis and Tiny URL approaches for URL features extraction. The accuracy metric obtained surpasses 98% for Extra Tree algorithm and can achieve 99% for Deep Neural Network model. © 2022 IEEE.
URL	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134218804&doi=10.1109%2fISDFS55398.2022.9800795&partnerID=40&md5=65081aa39ffad3c05f668a253be030fb
DOI	10.1109/ISDFS55398.2022.9800795