Detection of Phishing Websites from URLs by using Classification Techniques on WEKA - Geyik, Buket; Erensoy, Kubra; Kocyigit, Emre (IEEE, 2021)
The Internet is getting stronger day by day and it makes our lives easier with many applications that are executed on cyberworld. However, with the development of the internet, cyber-attacks have increased gradually and identity thefts have emerged. It is a type of fraud committed by intruders by using fake web pages to access people's private information such as userid, password, credit card number and bank account numbers, etc. These scammers can also send e-mail from many important institutions and organizations by using phishing attacks which imitate these web pages and acts as if they are original. Traditional security mechanisms can not prevent these attacks because they directly target the weakest part of connection : end-users. Machine learning technology has been used to detect and prevent this type of intrusions. The anti-phishing method has been developed by detecting the attacks made with the technologies used. In this paper, we combined the websites used by phishing attacks into a dataset, then we obtained some results using 4 classification algorithms with this dataset. The experimental results showed that the proposed systems give very good accuracy levels for the detection of these attacks.
Keywords: phishing attacks; machine learning; classification algorithms; phishing detection; cybersecurity; Computer Science; Engineering