This paper is published in Volume-9, Issue-6, 2024
Area
Malicious URL Detection
Author
Dev Kumar, E. Deepan Kumar, Aarya D. Roy, Aftab Alam, Harsh Vardhan
Org/Univ
Excel Engineering College, Komarapalayam, Tamil Nadu, India
Pub. Date
27 February, 2024
Paper ID
V9I6-1162
Publisher
Keywords
URL, Malicious URL Detection, Machine Learning, Feature Extraction, XGBoost Algorithm

Citationsacebook

IEEE
Dev Kumar, E. Deepan Kumar, Aarya D. Roy, Aftab Alam, Harsh Vardhan. Machine learning approach to detect malicious URL using XGBoost algorithm, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Dev Kumar, E. Deepan Kumar, Aarya D. Roy, Aftab Alam, Harsh Vardhan (2024). Machine learning approach to detect malicious URL using XGBoost algorithm. International Journal of Advance Research, Ideas and Innovations in Technology, 9(6) www.IJARIIT.com.

MLA
Dev Kumar, E. Deepan Kumar, Aarya D. Roy, Aftab Alam, Harsh Vardhan. "Machine learning approach to detect malicious URL using XGBoost algorithm." International Journal of Advance Research, Ideas and Innovations in Technology 9.6 (2024). www.IJARIIT.com.

Abstract

There are over a billion websites today for the people to visit. People uses the websites to make their works easy but there is a high chance to fall the phishing domain over the internet that inject malware to the client’s system or trick them to get their personal details. We will discuss about the machine learning method to classify these URLs in order to prevent people from visiting malicious URLs and improve the security of surfing over the internet. XGBoost algorithm and its performance has been discussed and how it uses the several features of URL to classify and detect the malicious URLs.