Muhammed Ali KOŞAN, Oktay YILDIZ, Hacer Karacan

Kimlik avı web sitelerinin tespitinde makine öğrenmesi algoritmalarının karşılaştırmalı analizi

Web uygulamalarının kullanım oranındaki artış ile birlikte sayısı artan kötücül web siteleri ve saldırılar, son kullanıcıya ciddi zararlar vermektedir. Kişisel ve hassas bilgilerin çalınmasına yönelik bu saldırılardan biri Kimlik Avı saldırısıdır. Yayımlanan güvenlik raporlarında son yıllarda milyonlarca yeni kimlik avı sahteciliği yapan web sayfası tespit edildiği ifade edilmektedir. Böylesi kritik bir durumda bu web sayfalarının tespiti büyük önem arz etmektedir. Bu çalışmada, bir veri kümesi ile birlikte literatürde bulunan makine öğrenmesi sınıflandırma algoritmaları kullanılarak karşılaştırmalı analiz yapılmıştır. Analiz sonuçları, Kimlik Avı Sahteciliği çalışmalarında kullanılan sınıflandırma algoritmalarının hangi koşullarda tercih edilmesi gerektiği hakkında farklı parametreler bulunduğunu göstermektedir.

Anahtar Kelimeler:

Kimlik avı saldırıları, Makine öğrenmesi, Sınıflandırma algoritmaları, Değerlendirme ölçütleri

Comparative analysis of machine learning algorithms in detection of phishing websites

The increasing number of malicious web sites and attacks, along with the increase in the usage rate of web applications, cause severe damage to the end user. One of these attacks aimed at stealing personal and sensitive information is the Phishing Attack. In the published security reports, it is stated that in recent years there has been millions of web pages that have made new phishing scams. In such a critical situation, the identification of these web pages is of great importance. In this study, a comparative analysis was made on a mentioned dataset using machine learning classification algorithms in the literature. The results of the analysis show that the classification algorithms used have different parameters about which conditions should be preferred in the studies on Phishing Fraud.

Keywords:

Phishing attacks, Machine learning, Classification algorithms, Assessment measures,

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