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Using natural language processing for phishing detection

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dc.contributor.authorJonker, Richard A.A.
dc.contributor.authorPoudel, Roshan
dc.contributor.authorPedrosa, Tiago
dc.contributor.authorLopes, Rui Pedro
dc.date.accessioned2022-04-05T08:35:03Z
dc.date.available2022-04-05T08:35:03Z
dc.date.issued2021
dc.description.abstractWe live in a world where computers are constantly changing the way we do things. People spend many hours on their phones or computers, whether it be for work or leisure purposes. The danger is that these unsuspecting users can be targeted for attacks at any time and can fall victim to many types of scams or phishing attacks. These attacks can be harmful to the user by getting valuable credentials, money or even installing malicious software on their devices, all while the user is unaware of what has just happened. In a business environment these can lead to mass data breeches which could end up costing a company millions of euros. Many users are not trained to recognize phishing texts, so an alternative solution is needed to help prevent users from falling into these traps. In this paper we will be investigating Natural Language Processing (NLP), a subsection of Machine Learning (ML) to try generate solutions to the problem of phishing. We will investigating different NLP solutions: Word2Vec, Doc2Vec and BERT, and different ML solutions: RNN, LSTM, CNN and TD-IDF. All of these different approaches provide good classification results ranging from f1-scores of 90.03–98.94.pt_PT
dc.description.sponsorshipThis work was partially supported by the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within project “CybersSeCIP” (NORTE-01-0145-FEDER-000044)pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationJonker, Richard Adolph Aires; Poudel, Roshan; Pedrosa, Tiago; Lopes, Rui Pedro (2021). Using natural language processing for phishing detection. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 540-552. ISBN 978-3-030-91884-2pt_PT
dc.identifier.doi10.1007/978-3-030-91885-9_40pt_PT
dc.identifier.isbn978-3-030-91884-2
dc.identifier.urihttp://hdl.handle.net/10198/25337
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCybersecuritypt_PT
dc.subjectMachine learningpt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectPhishingpt_PT
dc.subjectNatural language processingpt_PT
dc.titleUsing natural language processing for phishing detectionpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.endPage552pt_PT
oaire.citation.startPage540pt_PT
oaire.citation.titleOptimization, learning algorithms and applications: first International Conference, OL2A 2021pt_PT
oaire.citation.volume1488pt_PT
person.familyNamePedrosa
person.familyNameLopes
person.givenNameTiago
person.givenNameRui Pedro
person.identifier.ciencia-idB81E-0583-AEDF
person.identifier.ciencia-id8E14-54E4-4DB5
person.identifier.orcid0000-0003-4873-2705
person.identifier.orcid0000-0002-9170-5078
person.identifier.ridG-2249-2011
person.identifier.scopus-author-id35318153700
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationfee2835e-2230-4414-a58e-bcba895d1f0b
relation.isAuthorOfPublicatione1e64423-0ec8-46ee-be96-33205c7c98a9
relation.isAuthorOfPublication.latestForDiscoverye1e64423-0ec8-46ee-be96-33205c7c98a9

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