Phishing attack types and mitigation: A survey

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dc.contributor.authorAlghenaim, Mohammed Fahad
dc.contributor.authorBakar, Nur Azaliah Abu
dc.contributor.authorAbdul Rahim, Fiza
dc.contributor.authorVanduhe, Vanye Zira
dc.contributor.authorAlkawsi, Gamal
dc.date.accessioned2023-06-19T10:55:51Z
dc.date.available2023-06-19T10:55:51Z
dc.date.issued2023en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractThe proliferation of the internet and computing devices has drawn much attention during the Covid-19 pandemic stay home and work, and this has led the organization to adapt to staying home. Also, to let the organization work due to the infrastructure for working on proxy during the pandemic. The alarming rate of cyber-attacks, which through this study infer that phishing is one of the most effective and efficient ways for cyber-attack success. In this light, this study aims to study phishing attacks and mitigation methods in play, notwithstanding analysing performance metrics of the current mitigation performance metrics. Results indicate that business enterprises and educational institutions are the most hit using email (social engineering) and web app phishing attacks. The most effective mitigation methods are training/awareness campaigns on social engineering and using artificial intelligence/machine learning (AI/ML). To gain zero or 100% phishing mitigation, AI/ML need to be applied in large scale to measure its efficiency in phishing mitigation.en_US
dc.identifier.doi10.1007/978-981-99-0741-0_10en_US
dc.identifier.scopus2-s2.0-85152077444en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6631
dc.identifier.urihttps://doi.org/10.1007/978-981-99-0741-0_10
dc.identifier.volume165en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes on Data Engineering and Communications Technologiesen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial intelligence; Machine learning; Mitigation; Phishing; Social engineeringen_US
dc.titlePhishing attack types and mitigation: A surveyen_US
dc.typeBook Chapteren_US

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