Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1562
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dc.contributor.authorAtimorathanna, D.N.-
dc.contributor.authorRanaweera, T.S.-
dc.contributor.authorPabasara, R.A.H.D.-
dc.contributor.authorPerera, J.R.-
dc.contributor.authorAbeywardena, K.Y.-
dc.date.accessioned2022-03-10T10:34:36Z-
dc.date.available2022-03-10T10:34:36Z-
dc.date.issued2020-12-10-
dc.identifier.isbn978-1-7281-8412-8-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1562-
dc.description.abstractPhishing attacks have been identified by researchers as one of the major cyber-attack vectors which the general public has to face today. Although many vendors constantly launch new anti-phishing products, these products cannot prevent all the phishing attacks. The proposed solution, “NoFish” is a total anti-phishing protection system created especially for end-users as well as for organizations. This paper proposes a machine learning & computer vision-based approach for intelligent phishing detection. In this paper, a realtime anti-phishing system, which has been implemented using four main phishing detection mechanisms, is proposed. The system has the following distinguishing properties from related studies in the literature: language independence, use of a considerable amount of phishing and legitimate data, real-time execution, detection of new websites, detecting zero hour phishing attacks and use of feature-rich classifiers, visual image comparison, DNS phishing detection, email client plugin and especially the overall system is designed using a level-based security architecture to reduce the time-consumption. Users can simply download the NoFish browser extension and email plugin to protect themselves, establishing a relatively secure browsing environment. Users are more secure in cyberspace with NoFish which depicts a 97% accuracy level.en_US
dc.language.isoenen_US
dc.publisher2020 2nd International Conference on Advancements in Computing (ICAC), SLIITen_US
dc.relation.ispartofseriesVol.1;-
dc.subjectCyber-attacken_US
dc.subjectAnti-phishingen_US
dc.subjectInformation Securityen_US
dc.subjectMachine Learningen_US
dc.subjectVisual similarityen_US
dc.subjectFeature extractionen_US
dc.subjectNatural Language Processingen_US
dc.titleNoFish; Total Anti-Phishing Protection Systemen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC51239.2020.9357145en_US
Appears in Collections:2nd International Conference on Advancements in Computing (ICAC) | 2020
Department of Computer Systems Engineering-Scopes

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