Please use this identifier to cite or link to this item:
https://rda.sliit.lk/handle/123456789/1562
Title: | NoFish; Total Anti-Phishing Protection System |
Authors: | Atimorathanna, D.N. Ranaweera, T.S. Pabasara, R.A.H.D. Perera, J.R. Abeywardena, K.Y. |
Keywords: | Cyber-attack Anti-phishing Information Security Machine Learning Visual similarity Feature extraction Natural Language Processing |
Issue Date: | 10-Dec-2020 |
Publisher: | 2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT |
Series/Report no.: | Vol.1; |
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. |
URI: | http://rda.sliit.lk/handle/123456789/1562 |
ISBN: | 978-1-7281-8412-8 |
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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