Phishing Detection System Using Extreme Learning Machines with Different Activation Function based on Majority Voting

Authors : Murat UÇAR

Pages : 401-414

Doi:10.2339/politeknik.1098037

View : 116 | Download : 8

Publication Date : 2023-03-27

Article Type : Research Paper

Abstract :Phishing is a type of software-based cyber-attack carried out to steal private information such as login credentials, user passwords, and credit card information. When the security reports published in recent years are examined, it is seen that there are millions of phishing spoofing web pages. Therefore, in this study, it is aimed to develop an effective phishing detection model. In the study, an extreme learning machine based model using different activation functions such as sine, hyperbolic tangent function, rectified linear unit, leaky rectified linear unit and exponential linear unit was proposed and comparative analyses were made. In addition, the performances of the models when combined with the majority vote were also evaluated and it was seen that the highest accuracy value of 97.123% was obtained when the three most successful activation functions were combined with the majority vote. Experimental results show the effectiveness and applicability of the model proposed in the study.  
Keywords : Kimlik avı tespiti, aşırı makine öğrenimi, çoğunluk oylaması

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