Kimlik avı web sitelerinin tespitinde makine öğrenmesi algoritmalarının karşılaştırmalı analizi

Yıl 2018, Cilt: 24 Sayı: 2, 276 - 282, 30.04.2018

Muhammed Ali Koşan , Oktay Yıldız , Hacer Karacan

Öz

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

Öz

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.

Anahtar Kelimeler

Phishing attacks, Machine learning, Classification algorithms, Assessment measures

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