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Kötücül Web Sayfalarının Tespitinde Doc2Vec Modeli ve Makine Öğrenmesi Yaklaşımı

Günümüzde birçok işlem dijital ortama taşınmakta ve verilerimizi bu ortamda korumak zorlaşmaktadır. Birçok şeyin internete bağlı olması web güvenliği büyük bir sorun olarak ortaya koymaktadır. İnternet kaynaklı saldırıları başlatmanın en yaygın yolu da kötü amaçlı URL adreslerini kullanmaktır. Kötücül faaliyette bulunan korsanlar bu amaçla hazırladıkları web sitelerini kullanarak birçok veriyi elde etmektedirler. Bu tür kötü amaçlı URL adreslerini veya web sitelerini tespit etmenin geleneksel yolu kara liste kullanmaktır. Ancak bu yöntem yeni oluşturulan kötü amaçlı URL’lerin tespit edilmesinde başarılı olmamaktadır. Bu çalışmada, kötücül URL adreslerinin tespitinde verimliliği artırmak ve kara liste gibi bir takım veri tabanlarına bağımlılığı önlemek için makine öğrenmesi kullanan bir yaklaşım önerildi. Makine öğreniminde sınıflandırma için farklı algoritmalar denenirken, özellik çıkarımı için Doc2Vec yaklaşımı kullanılmıştır. Sadece URL adreslerinden elde edilen özellikler kullanılarak sınıflandırma yapılmaktadır. ISCX2016URL veri seti ile yapılan testlerin birinci aşamasında URL adresinin kötücül ve iyicil olarak sınıflandırma için Logistic Regresyon algoritması ile %99,2 doğruluk yakalanırken, kesinlik, duyarlılık ve F-skoru değerlerinde sırasıyla %98,9, %99,1 ve %99,2 değerleri yakalanmıştır. Testlerin ikinci aşamasında ise kötücül URL adreslerinin spam, kimlik avı, kötücül amaçlı yazılım dağıtan ve tahrif edilmiş sınıflarına aitlikleri test edilmiştir. Sonuçta SVC sınıflandırıcı ile %88,1 doğruluk ile kötücül URL adresleri sınıflandırılmıştır. Sonuçta ortaya çıkan modeli herhangi bir vekil sunucuda veya bir ağ denetleyici platforma üzerinde uygulamak mümkündür.

Anahtar Kelimeler:

Tekdüzen Kaynak Bulucu(URL), Doc2Vec, web güvenliği, makine öğrenmesi, URL filtreleme.

A Detection Method for Malicious Web Pages using Doc2vec Model and Machine Learning

Today, many transactions are transferred to the digital environment and it is difficult to protect our data in this environment. Due to the fact that many things are connected to the internet, web security is emerging as a big problem. The most common way to initiate Internet-borne attacks is by using malicious URL addresses. Hackers engaged in malicious activity obtain a lot of data by using the websites they have prepared for this purpose. The traditional way to detect such malicious URL addresses or websites is by using a blacklist. However, this method does not succeed in detecting newly created malicious URLs. In this study, an approach using machine learning is proposed to increase efficiency in detecting malicious URLs and prevent dependence on some databases such as blacklists. While different machine learning algorithms were tried for classification, Doc2Vec approach was used for feature extraction. Classification is made using only the features obtained from URL addresses. In the first stage of the tests conducted with the ISCX2016URL data set, URLs were classified as malicious or benign. With the Logistic Regression algorithm, 99.2% accuracy was achieved, while the precision, sensitivity and F-score values were 98.9%, 99.1% and 99.2%, respectively. In the second stage of the tests, the malicious URLs belonging to the classes spam, phishing, malware and defacement were tested. Malicious URLs were classified by SVC with 88.1% accuracy. It is possible to implement the resulting model on any Proxy server or on a network controller platform

Keywords:

Uniform resource locators (URLs), Doc2Vec, web security, machine learning, URL filtering,

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