Publication:
Phishing Attack Types and Mitigation: A Survey

Simple item page
dc.citedby2
dc.contributor.authorAlghenaim M.F.en_US
dc.contributor.authorBakar N.A.A.en_US
dc.contributor.authorAbdul Rahim F.en_US
dc.contributor.authorVanduhe V.Z.en_US
dc.contributor.authorAlkawsi G.en_US
dc.contributor.authorid57226694830en_US
dc.contributor.authorid56330254700en_US
dc.contributor.authorid57981022800en_US
dc.contributor.authorid57204520791en_US
dc.contributor.authorid57191982354en_US
dc.date.accessioned2024-10-14T03:21:38Z
dc.date.available2024-10-14T03:21:38Z
dc.date.issued2023
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. � 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-981-99-0741-0_10
dc.identifier.epage153
dc.identifier.scopus2-s2.0-85152077444
dc.identifier.spage131
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85152077444&doi=10.1007%2f978-981-99-0741-0_10&partnerID=40&md5=b8328c0bc61d590d3dc56dd93411231e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34674
dc.identifier.volume165
dc.pagecount22
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes on Data Engineering and Communications Technologies
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectMitigation
dc.subjectPhishing
dc.subjectSocial engineering
dc.subjectComputer crime
dc.subjectCrime
dc.subjectNetwork security
dc.subjectComputing devices
dc.subjectCyber-attacks
dc.subjectInternet devices
dc.subjectMachine-learning
dc.subjectMitigation
dc.subjectMitigation methods
dc.subjectPerformance metrices
dc.subjectPhishing
dc.subjectPhishing attacks
dc.subjectSocial engineering
dc.subjectMachine learning
dc.titlePhishing Attack Types and Mitigation: A Surveyen_US
dc.typeBook chapteren_US
dspace.entity.typePublication
Files
Collections
SCOPUS