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dc.contributor.authorTung, SP
dc.contributor.authorWong, KY
dc.contributor.authorKuzminykh, Ievgeniia
dc.contributor.authorBakhshi, Taimur
dc.contributor.authorGhita, B
dc.date.accessioned2023-03-17T14:18:41Z
dc.date.available2023-03-17T14:18:41Z
dc.date.issued2022
dc.identifier.isbn9783030977764
dc.identifier.urihttps://pearl.plymouth.ac.uk/handle/10026.1/20582
dc.description.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%.

dc.format.extent493-505
dc.publisherSpringer International Publishing
dc.relation.ispartofLecture Notes in Computer Science
dc.titleUsing a Machine Learning Model for Malicious URL Type Detection
dc.typechapter
plymouth.volume13158 LNCS
plymouth.publication-statusPublished
dc.identifier.doi10.1007/978-3-030-97777-1_41
plymouth.organisational-group|Plymouth
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering|School of Engineering, Computing and Mathematics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA
plymouth.organisational-group|Plymouth|Users by role
plymouth.organisational-group|Plymouth|Users by role|Academics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA|UoA11 Computer Science and Informatics
dc.date.updated2023-03-17T14:18:39Z
dc.rights.embargodate10000-01-01
dc.rights.embargoperiodforever
rioxxterms.versionofrecord10.1007/978-3-030-97777-1_41


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