期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：6
出版社：Journal of Theoretical and Applied
摘要：Internet use is growing every day, accessing a website via its URL (Uniform Resource Locator) address is a daily task, but not all websites are benign to be accessed without any fear from malicious aims - not matter where those websites are being accessed from (Web Browsers, e-mails body, chat application, SMS, VoIP) neither the nature of the operating system or the device. Our thesis aim is being able to detect the kind of websites that try to steal any user�s (normal users, communities, societies, laboratories, etc.) personal information like name, date of birth, e-mail, credentials, login and passwords from e-banking services for example or any other web services. Unlike traditional techniques that consists of penetrating data sources of web services providers by decrypting algorithms, the man idea of this kind of criminal activities is letting the victims give those informations unconsciously, by creating fake emails or websites that looks very similar of original ones and tell victims to fill some forms with their informations for some fake reason, this technique is called phishing. This article aims to discuss some used techniques in detecting phishing websites, like Black-list based, Lexical based, Content based and Security and Identity based methods combined with some machine learning classifier to classify if a test URL is a safe or phishing website and to propose a new hybrid framework to detect phishing web pages from only their URL without need to access it visually with a browser. The data used for building the model and classification is a collection of active phishing websites gathered from PhishTank[1].
关键词：Machine Learning; Malicious URL Detection; Network Security Intelligence; Phishing; Smart Systems and Communication
