Show simple item record

dc.contributor.authorTung, SP
dc.contributor.authorWong, KY
dc.contributor.authorKuzminykh, I
dc.contributor.authorBakhshi, T
dc.contributor.authorGhita, B
dc.contributor.editorKoucheryavy Y
dc.contributor.editorBalandin SI
dc.contributor.editorAndreev S
dc.date.accessioned2023-03-17T14:18:41Z
dc.date.available2023-03-17T14:18:41Z
dc.date.issued2022
dc.identifier.isbn9783030977764
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
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.subject46 Information and Computing Sciences
dc.subject4604 Cybersecurity and Privacy
dc.titleUsing a Machine Learning Model for Malicious URL Type Detection
dc.typeconference
dc.typeConference Proceeding
plymouth.volume13158 LNCS
plymouth.publisher-urlhttp://dx.doi.org/10.1007/978-3-030-97777-1_41
plymouth.publication-statusPublished
plymouth.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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.identifier.eissn1611-3349
dc.rights.embargoperiodforever
rioxxterms.versionofrecord10.1007/978-3-030-97777-1_41


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record


All items in PEARL are protected by copyright law.
Author manuscripts deposited to comply with open access mandates are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author.
Theme by 
Atmire NV