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https://rda.sliit.lk/handle/123456789/1562
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DC Field | Value | Language |
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dc.contributor.author | Atimorathanna, D.N. | - |
dc.contributor.author | Ranaweera, T.S. | - |
dc.contributor.author | Pabasara, R.A.H.D. | - |
dc.contributor.author | Perera, J.R. | - |
dc.contributor.author | Abeywardena, K.Y. | - |
dc.date.accessioned | 2022-03-10T10:34:36Z | - |
dc.date.available | 2022-03-10T10:34:36Z | - |
dc.date.issued | 2020-12-10 | - |
dc.identifier.isbn | 978-1-7281-8412-8 | - |
dc.identifier.uri | http://rda.sliit.lk/handle/123456789/1562 | - |
dc.description.abstract | Phishing 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.iso | en | en_US |
dc.publisher | 2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT | en_US |
dc.relation.ispartofseries | Vol.1; | - |
dc.subject | Cyber-attack | en_US |
dc.subject | Anti-phishing | en_US |
dc.subject | Information Security | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Visual similarity | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Natural Language Processing | en_US |
dc.title | NoFish; Total Anti-Phishing Protection System | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/ICAC51239.2020.9357145 | en_US |
Appears in Collections: | 2nd International Conference on Advancements in Computing (ICAC) | 2020 Department of Computer Systems Engineering-Scopes |
Files in This Item:
File | Description | Size | Format | |
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NoFish_Total_Anti-Phishing_Protection_System.pdf Until 2050-12-31 | 382.77 kB | Adobe PDF | View/Open Request a copy |
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