Using a Machine Learning Model for Malicious URL Type Detection

Suet Ping Tung, Ka Yan Wong, Ievgeniia Kuzminykh, Taimur Bakhshi, Bogdan Ghita

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddCyfraniad i gynhadleddadolygiad gan gymheiriaid

Crynodeb

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%.
Iaith wreiddiolSaesneg
TeitlInternet of Things, Smart Spaces, and Next Generation Networks and Systems
Is-deitl21st International Conference, NEW2AN 2021, and 14th Conference, ruSMART 2021, St. Petersburg, Russia, August 26–27, 2021, Proceedings
GolygyddionYevgeni Koucheryavy, Sergey Balandin, Sergey Andreev
CyhoeddwrSpringer
Tudalennau493-505
Nifer y tudalennau13
ISBN (Electronig)978-3-030-97777-1
ISBN (Argraffiad)978-3-030-97776-4
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 16 Maw 2022
Cyhoeddwyd yn allanolIe
Digwyddiad21st International Conference on Next Generation Wired/Wireless Networks and Systems - Online, St, Petersburg, Rwsia
Hyd: 30 Awst 202131 Awst 2021

Cyfres gyhoeddiadau

EnwLecture Notes in Computer Science
CyhoeddwrSpringer
Cyfrol13158
ISSN (Argraffiad)0302-9743
ISSN (Electronig)1611-3349

Cynhadledd

Cynhadledd21st International Conference on Next Generation Wired/Wireless Networks and Systems
Teitl crynoNEW2AN 2021
Gwlad/TiriogaethRwsia
DinasSt, Petersburg
Cyfnod30/08/2131/08/21

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