Makine Öğrenmesi Yöntemleri Kullanarak Web Uygulama Saldırılarının Tespitinde Genetik Öznitelik Seçimi Yaklaşımı

Year 2021, Volume: 14 Issue: 2, 109 - 119, 22.12.2021

Hüseyin Ahmetoğlu , Resul Daş

https://doi.org/10.54525/tbbmd.1018465

Abstract

İnternet üzerindeki uygulamalar kodlama kaynaklı bir takım güvenlik endişelerini barındırırlar. Zayıflıklar veya güvenlik açıkları, suçluların hassas verileri çalmak için veri tabanlarına doğrudan ve genel erişim elde etmesine olanak tanır. Bu çalışmada, web uygulama saldırılarının hibrit saldırı tespit sistemleri ile daha kolay ve daha doğru tespiti için sezgisel öznitelik seçimi ve makine öğrenmesine dayanan bir yaklaşım önerilmektedir. CIC-IDS2017 ve CSE-CIC-IDS2018 veri setlerindeki web uygulama saldırıları ve normal akış örnekleri bir dizi veri ön işleme aşaması sonrası birleştirilerek ve yeni bir veri seti oluşturuldu. Genetik Algoritma ve Lojistik Regresyon kullanılarak ortalama karesel hata ve öznitelik sayısı optimizasyonu gerçekleştirilip sonuçlar beş farklı makine öğrenmesi algoritması ile test edildi. Elde edilen sonuçlar incelendiğinde, öznitelik sayısının %85 oranında azaltılmasına rağmen sınıflandırmadaki başarım oranlarının %99 seviyesinde kaldığı gözlemlenmiştir.

Keywords

web uygulama saldırısı, makine öğrenmesi, genetik algoritma, öznitelik seçimi, saldırı tespit sistemi

Genetic Feature Selection Approach in Detection of Web Application Attacks Using Machine Learning Methods

Abstract

Applications on the Internet have some coding-related security concerns. Weaknesses or vulnerabilities allow criminals to gain direct and public access to databases to steal sensitive data. This study proposes an approach based on heuristic feature selection and machine learning for easier and more accurate detection of web application attacks with hybrid intrusion detection systems. Web application attacks and benign flow examples in CIC-IDS2017 and CSE-CIC-IDS2018 datasets were combined after a series of data preprocessing stages, and a new dataset was created. Using Genetic Algorithm and Logistic Regression, mean square error and feature count optimization were performed, and the results were tested with five different machine learning algorithms. When the results obtained were examined, it was observed that the success rate in classification remained at the level of 99%, although the number of features was reduced by 85%

Keywords

web application attack, machine learning, genetic algorithm, feature selection, intrusion detection system

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