Predicting phishing websites based on self-structuring neural network

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dc.contributor.authorMohammad, Rami M.
dc.contributor.authorThabtah, Fadi
dc.contributor.authorMcCluskey, Lee
dc.date.accessioned2020-10-01T14:20:12Z
dc.date.available2020-10-01T14:20:12Z
dc.date.copyright2013
dc.date.issued2014
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article is published in Neural Computing and Applications (2014), available online at: https://doi.org/10.1007/s00521-013-1490-zen_US
dc.description.abstractInternet has become an essential component of our everyday social and financial activities. Nevertheless, internet users may be vulnerable to different types of web threats, which may cause financial damages, identity theft, loss of private information, brand reputation damage and loss of customer’s confidence in e-commerce and online banking. Phishing is considered as a form of web threats that is defined as the art of impersonating a website of an honest enterprise aiming to obtain confidential information such as usernames, passwords and social security number. So far, there is no single solution that can capture every phishing attack. In this article, we proposed an intelligent model for predicting phishing attacks based on artificial neural network particularly self-structuring neural networks. Phishing is a continuous problem where features significant in determining the type of web pages are constantly changing. Thus, we need to constantly improve the network structure in order to cope with these changes. Our model solves this problem by automating the process of structuring the network and shows high acceptance for noisy data, fault tolerance and high prediction accuracy. Several experiments were conducted in our research, and the number of epochs differs in each experiment. From the results, we find that all produced structures have high generalization ability. © Springer-Verlag London 2013.en_US
dc.identifier.citationMohammad, R. M., Thabtah, F., & McCluskey, L. (2014). Predicting phishing websites based on self-structuring neural network. Neural Computing and Applications, 25(2), 443–458. https://doi.org/10.1007/s00521-013-1490-zen_US
dc.identifier.issn09410643
dc.identifier.urihttp://dx.doi.org/10.1007/s00521-013-1490-z
dc.identifier.urihttp://hdl.handle.net/20.500.12519/248
dc.language.isoenen_US
dc.publisherSpringer Londonen_US
dc.relationAuthors Affiliations : Mohammad, R.M., School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom; Thabtah, F., E-Bussiness Department, Canadian University of Dubai, Dubai, United Arab Emirates; McCluskey, L., School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
dc.relation.ispartofseriesNeural Computing and Applications;Vol. 25, no. 2
dc.rightsLicense to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holderCopyright :© Springer-Verlag London 2013
dc.rights.licenseLicense Number: 5204721286933 License date: Dec 09, 2021
dc.rights.urihttps://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=69809290-1f25-4548-85a5-32f252273751
dc.subjectData miningen_US
dc.subjectFault toleranceen_US
dc.subjectForecastingen_US
dc.subjectInformation disseminationen_US
dc.subjectInterneten_US
dc.subjectMobile securityen_US
dc.subjectNeural networksen_US
dc.subjectSecurity of dataen_US
dc.subjectSocial networking (online)en_US
dc.subjectWebsitesen_US
dc.subjectConfidential informationen_US
dc.subjectContinuous problemen_US
dc.subjectGeneralization abilityen_US
dc.subjectIntelligent modelingen_US
dc.subjectPhishingen_US
dc.subjectSelf structuring neural networksen_US
dc.subjectSocial security numbersen_US
dc.subjectWeb threaten_US
dc.subjectComputer crimeen_US
dc.titlePredicting phishing websites based on self-structuring neural networken_US
dc.typeArticleen_US
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