Bilal ALAGHA

550233

N-Gram Tabanlı Tahmin Modeli ile XSS Saldırısı Algılama

Teknolojideki hızlı gelişmeler web uygulamalarını güçlendirmiştir. Bu arada, web uygulamalarında Siteler Arası Betik Çalıştırma (XSS olarak da bilinir) güvenlik açıklarının varlığı, kullanıcılar için bir endişe haline gelmiştir. Çok sayıda mevcut algılama yaklaşımına rağmen saldırganlar yıllardır XSS güvenlik açıklarından yararlanarak internet kullanıcılarına zarar veriyor. Bu makalede, web uygulamalarındaki XSS Saldırısını tespit etmek için metin madenciliği tabanlı bir yaklaşım tanıtılmaktadır. Bu yaklaşım, halka açık bir kaynak kod dosyalarından bir dizi özellik çıkarmak için oluşturulmuştur. Bu dosyalar, daha sonra bir tahmin modeli oluşturmaya yardımcı olmak için kullanılır. Bulgular, N-Gram ve Kelime Simgeleştirme arasında doğruluk, modeli oluşturmak için harcanan zaman ve AUC-ROC eğrisi birkaç karşılaştırma içerir. Sonuç olarak N-Gram Kelime Simgeleştirmesinden daha iyi performans göstermektedir.
Anahtar Kelimeler:

XSS Saldırıları, Tahmin Modeli, Makine Öğrenme, Kelime Simgeleştirme, N-Gram, Algoritma

XSS Attack Detection with N-Gram Based Prediction Model

The increment developments in technology has empowered the web applications. Meanwhile, the existence of Cross-Site Scripting (XSS) vulnerabilities in web applications has become a concern for users. In spite of the numerous current detection approaches, attackers have been exploiting XSS vulnerabilities for years, causing harm to the internet users. In this paper, a text-mining based approach to detect XSS attacks in web applications is introduced. This approach is built to extract a set of features from a publicly available source code files, which are then used to build a prediction model. The findings include few comparisons between Word Tokenization and N-Gram in accuracy, time spend to build the model and AUC-ROC curve. The results show that N-Gram tokenization outperforms the Word Tokenization.
Keywords:

XSS Attacks, Predictive Model, Machine Learning, Word Tokenization, N-Gram, Algorithm,

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Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi-Cover
  • ISSN: 2687-606X
  • Yayın Aralığı: Yılda 2 Sayı
  • Başlangıç: 31.01.2020
  • Yayıncı: Eskişehir Osmangazi Üniversitesi
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