Jar Malware Detection with XGBoost Algorithm Based on Binary Particle Swarm Optimization Feature Selection

Year 2023, Volume: 10 Issue: 1, 140 - 152, 31.05.2023

Mahmut Tokmak

https://doi.org/10.35193/bseufbd.1194460

Cited By: 1

Abstract

Attacks with malware using the Java language have increased rapidly in recent years. With these increases, the damage that malware can cause to people and institutions has led researchers to develop and test different machine learning techniques to improve and strengthen automatic detection systems. In this study, a hybrid system is proposed for the detection of malicious Jar files, which uses binary particle swarm optimization based feature selection and classification with XGBoost algorithm. While minimizing is achieved in the binary particle swarm optimization algorithm, the random forest algorithm is used in the fitness function. With feature selection, it is aimed to increase speed and performance by reducing computational load on classification algorithm. In proposed model, training and tests were carried out by performing 10-fold cross validation. In detection mechanism made with the XGBoost algorithm, the performance of the model established with Accuracy, Precision, F1-Score, Recall metrics has been demonstrated. In order to evaluate performance of the proposed model, tests were made with AdaBoost, Gradient Boosting, Support Vector Machines, Artificial Neural Networks, Naïve Bayes methods and the results were compared. Experimental results showed that the proposed binary particle swarm optimization-based feature selection and hybrid model classifying with the XGBoost algorithm was more successful in detecting Jar malware than the compared models with an accuracy rate of 98.04%.

Keywords

Jar Malware Detection, XGBoost, Binary Particle Swarm Optimization, Feature Selection

XGBoost Algoritması ile İkili Parçacık Sürü Optimizasyonu Öznitelik Seçme Tabanlı Jar Kötü Amaçlı Yazılımlarının Tespiti

Abstract

Java dilini kullanan kötü amaçlı yazılımlarla gerçekleştirilen saldırılar, geçtiğimiz yıllarda hızla artış göstermeye başlamıştır. Bu artışlarla birlikte kötü amaçlı yazılımların kişilere ve kurumlara verebileceği zararlar araştırmacıları otomatik algılama sistemlerini geliştirerek güçlendirmek için farklı makine öğrenme teknikleri geliştirmeye ve test etmeye yöneltmiştir. Bu çalışmada kötü amaçlı Jar dosyalarının tespiti için ikili parçacık sürü optimizasyonu tabanlı öznitelik seçimi ve XGBoost algoritması ile sınıflandırma yapan hibrit bir sistem önerilmiştir. İkili parçacık sürü optimizasyonu algoritmasında minimizasyon sağlanırken kullanılan uygunluk fonksiyonunda rastgele orman algoritması kullanılmıştır. Öznitelik seçimi ile sınıflandırma algoritmasının üzerine düşen hesaplama yükü azaltılarak hız ve performans artırımı hedeflenmiştir. Önerilen modelde 10 kat çapraz doğrulama yapılarak eğitim ve testler gerçekleştirilmiştir. XGBoost algoritması ile yapılan tespit mekanizmasında doğruluk, kesinlik, F1-Skoru, duyarlılık metrikleri ile kurulan modelin performansı ortaya konulmuştur. Önerilen modelin performansının değerlendirilmesi amacıyla AdaBoost, Gradient Boosting, Destek Vektör Makineleri, Yapay Sinir Ağları, Naive Bayes yöntemleri ile testler yapılmış ve sonuçlar karşılaştırılmıştır. Deneysel sonuçlar, önerilen ikili parçacık sürü optimizasyonu tabanlı öznitelik seçimi ve XGBoost algoritması ile sınıflandırma yapan hibrit modelin kötü amaçlı Jar yazılım tespitinde %98.04 doğruluk oranı ile karşılaştırılan modellere göre daha başarılı olduğunu göstermiştir.

Keywords

Jar Malware Tespiti, XGBoost, İkili Parçacık Sürü Optimizasyonu, Öznitelik Seçme

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