Data analytics : intelligent anti-phishing techniques based on machine learning

Simple item page
dc.contributor.authorBaadel, Said
dc.contributor.authorLu, Joan
dc.date.accessioned2020-02-13T09:05:43Z
dc.date.available2020-02-13T09:05:43Z
dc.date.copyright2019en_US
dc.date.issued2019
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article is published in Journal of Information and Knowledge Management (2019), available online at: https://doi.org/10.1142/S0219649219500059.en_US
dc.description.abstractAccording to the international body Anti-Phishing Work Group (APWG), phishing activities have skyrocketed in the last few years and more online users are becoming susceptible to phishing attacks and scams. While many online users are vulnerable and naive to the phishing attacks, playing catch-up to the phishers' evolving strategies is not an option. Machine Learning techniques play a significant role in developing effective anti-phishing models. This paper looks at phishing as a classification problem and outlines some of the recent intelligent machine learning techniques (associative classifications, dynamic self-structuring neural network, dynamic rule-induction, etc.) in the literature that is used as anti-phishing models. The purpose of this review is to serve researchers, organisations' managers, computer security experts, lecturers, and students who are interested in understanding phishing and its corresponding intelligent solutions. This will equip individuals with knowledge and skills that may prevent phishing on a wider context within the community. © 2019 World Scientific Publishing Co.en_US
dc.identifier.citationBaadel, S., & Lu, J. (2019). Data analytics: Intelligent anti-phishing techniques based on machine learning. Journal of Information and Knowledge Management, 18(1). https://doi.org/10.1142/S0219649219500059en_US
dc.identifier.issn02196492
dc.identifier.urihttp://dx.doi.org/10.1142/S0219649219500059
dc.identifier.urihttps://hdl.handle.net/20.500.12519/136
dc.language.isoenen_US
dc.publisherWorld Scientific Publishing Co. Pte Ltden_US
dc.relationAuthors Affiliations: Baadel, S., Faculty of Communication Arts and Sciences, Canadian University Dubai, Dubai, United Arab Emirates, School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom; Lu, J., School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
dc.relation.ispartofseriesJournal of Information and Knowledge Management;Vol. 18, no. 1
dc.rightsPermission to reuse the abstract has been secured from World Scientific Publishing Co. Pte Ltd
dc.rights.holderCopyright : 2019 World Scientific Publishing Co.
dc.subjectClassificationen_US
dc.subjectData miningen_US
dc.subjectDynamic self-structuring neural networken_US
dc.subjectIntelligent anti-phishingen_US
dc.subjectMachine learningen_US
dc.subjectData analyticsen_US
dc.subjectLearning algorithmsen_US
dc.subjectLearning systemsen_US
dc.subjectSecurity of dataen_US
dc.subjectAnti-phishingen_US
dc.subjectAnti-phishing work groupsen_US
dc.subjectAssociative classificationen_US
dc.subjectIntelligent machineen_US
dc.subjectIntelligent solutionsen_US
dc.subjectMachine learning techniquesen_US
dc.subjectPhishing attacksen_US
dc.subjectSelf structuring neural networksen_US
dc.subjectComputer crimeen_US
dc.titleData analytics : intelligent anti-phishing techniques based on machine learningen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Access Instruction 136.pdf
Size:
55.89 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Department of General Education and Language