Çoğunluk Oylamasına Dayalı Farklı Etkinleştirme İşlevine Sahip Aşırı Öğrenme Makinelerini Kullanan Kimlik Avı Tespit Sistemi

Year 2023, , 401 - 414, 27.03.2023

Murat Uçar

https://doi.org/10.2339/politeknik.1098037

Abstract

Kimlik avı, oturum açma kimlik bilgileri, kullanıcı şifreleri, kredi kartı bilgileri gibi özel bilgileri çalmak amacıyla gerçekleştirilen yazılım tabanlı bir siber saldırı türüdür. Son yıllarda yayınlanan güvenlik raporları incelendiğinde milyonlarca kimlik avı sahteciliği yapan web sayfasının olduğu görülmektedir. Bu nedenle bu çalışmada etkili bir kimlik avı tespit modelinin geliştirilmesi amaçlanmıştır. Çalışmada sinüs, hiperbolik tanjant fonksiyonu, doğrultulmuş doğrusal birim, sızıntılı doğrultulmuş doğrusal birim ve üstel doğrusal birim gibi farklı aktivasyon fonksiyonlarının kullanıldığı aşırı öğrenme makineleri tabanlı bir model önerilmiş ve karşılaştırmalı analizler yapılmıştır. Ayrıca modellerin çoğunluk oyu ile birleştirildiğindeki performansları da değerlendirilmiş ve en yüksek doğruluk değerinin %97.123 ile en başarılı üç aktivasyon fonksiyonun çoğunluk oyu ile birleştirildiğinde elde edildiği görülmüştür. Deneysel sonuçlar, çalışmada önerilen modelin etkinliğini ve uygulanabilirliğini göstermektedir.

Keywords

Kimlik avı tespiti, aşırı makine öğrenimi, çoğunluk oylaması

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

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

Phishing detection, extreme machine learning, majority voting

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