A hybrid model to detect phishing-websites. (4th September 2022)

• Record Type: Journal Article
• Authors: Hafidi, Mohamed; Mahnane, Lamia
• Is Part Of: International journal of internet technology and secured transactions. Volume 12:Number 6(2022)
• Page Start: 483
• Page End: 502
• Publication Date: 2022-09-04
• Subjects: anti-phishing -- machine learning -- black-list -- white-list -- cyber security; Internet -- Security measures -- Periodicals; Computer security -- Periodicals; Information systems management -- Security measures -- Periodicals; Computer networks -- Security measures -- Periodicals; Information technology -- Security measures -- Periodicals; 005.805
• Languages: English
• ISSNs: 1748-569X

Abstract

Phishing and fraud sites has been widespread on the internet in recent times, in which that it is becoming a source of great concern and a serious cyber security problem, as internet fraudsters target sensitive data and personal information of users, especially the username and password. Numerous approaches has been proposed and used in order to prevent and reduce these phishing websites and attacks, and protect users and their privacy. In this paper, we proposed an anti-phishing system based on machine learning algorithms and classifiers, combined with white-list and black-list techniques. We used the C4.5 classifier that generate a decision tree that provides highly accurate results in deciding whether a site is a fraudulent website or a genuine website based on its URL. Our predictive model achieved a high accuracy rate of 83%. We have modelled our system in the form of a desktop application so that the user can add the URL of the website needed to be checked, and the results appear to the user. The system combination accuracy can exceed 95%, and the execution results prove that.