Using a Machine Learning Model for Malicious URL Type Detection
dc.contributor.author | Tung, SP | |
dc.contributor.author | Wong, KY | |
dc.contributor.author | Kuzminykh, Ievgeniia | |
dc.contributor.author | Bakhshi, Taimur | |
dc.contributor.author | Ghita, B | |
dc.date.accessioned | 2023-03-17T14:18:41Z | |
dc.date.available | 2023-03-17T14:18:41Z | |
dc.date.issued | 2022 | |
dc.identifier.isbn | 9783030977764 | |
dc.identifier.uri | https://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.extent | 493-505 | |
dc.publisher | Springer International Publishing | |
dc.relation.ispartof | Lecture Notes in Computer Science | |
dc.title | Using a Machine Learning Model for Malicious URL Type Detection | |
dc.type | chapter | |
plymouth.volume | 13158 LNCS | |
plymouth.publication-status | Published | |
dc.identifier.doi | 10.1007/978-3-030-97777-1_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.updated | 2023-03-17T14:18:39Z | |
dc.rights.embargodate | 10000-01-01 | |
dc.rights.embargoperiod | forever | |
rioxxterms.versionofrecord | 10.1007/978-3-030-97777-1_41 |