@inproceedings{db8d98ee495248b6839f7abbf264c84f,
title = "Using a Machine Learning Model for Malicious URL Type Detection",
abstract = "The world wide web, beyond its benefits, has also become a major platform for online criminal activities. Traditional protection methods against malicious URLs, such as blacklisting, remain a valid alternative, but cannot detect unknown sites, hence new methods are being developed for automatic detection, using machine learning approaches. This paper strengthens the existing state of the art by proposing an alternative machine learning approach, that uses a set of 14 lexical and host-based features but focuses on the typical mechanisms employed by malicious URLs. The proposed method employs random forest and decision tree as core mechanisms and is evaluated on a combined benign and malicious URL dataset, which indicates an accuracy of over 97%.",
keywords = "Malicious URL, Web security, Machine Learning, Phishing, Spamming, Malware, Lexical Feature, Traffic",
author = "Tung, {Suet Ping} and Wong, {Ka Yan} and Ievgeniia Kuzminykh and Taimur Bakhshi and Bogdan Ghita",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 21st International Conference on Next Generation Wired/Wireless Networks and Systems, NEW2AN 2021 ; Conference date: 30-08-2021 Through 31-08-2021",
year = "2022",
month = mar,
day = "16",
doi = "10.1007/978-3-030-97777-1_41",
language = "English",
isbn = "978-3-030-97776-4",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "493--505",
editor = "Yevgeni Koucheryavy and Sergey Balandin and Sergey Andreev",
booktitle = "Internet of Things, Smart Spaces, and Next Generation Networks and Systems",
address = "Germany",
}