NoFish; Total Anti-Phishing Protection System

Dhanushka Niroshan Atimorathanna, Tharindu Shehan Ranaweera, R.A.H. Devdunie Pabasara, Jayani Rukshila Perera, Kavinga Yapa Abeywardena
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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.
NoFish;全面反网络钓鱼保护系统
网络钓鱼攻击已被研究人员确定为当今公众不得不面对的主要网络攻击媒介之一。虽然许多厂商不断推出新的反网络钓鱼产品,但这些产品并不能阻止所有的网络钓鱼攻击。提出的解决方案“NoFish”是一个专门为最终用户和组织创建的全面反网络钓鱼保护系统。本文提出了一种基于机器学习和计算机视觉的网络钓鱼智能检测方法。本文提出了一种基于四种主要网络钓鱼检测机制的实时反网络钓鱼系统。与文献中相关研究相比,该系统具有以下特点:语言独立、使用了大量的网络钓鱼和合法数据、实时执行、检测新网站、检测零时网络钓鱼攻击和使用功能丰富的分类器、视觉图像比较、DNS网络钓鱼检测、电子邮件客户端插件,特别是整个系统采用了基于级别的安全架构设计,减少了时间消耗。用户只需下载NoFish浏览器扩展和电子邮件插件即可保护自己,建立一个相对安全的浏览环境。使用NoFish,用户在网络空间中更加安全,准确率达到97%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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