THE DETECTION OF ANDROID MALWARE APPLICATIONS WITH DEEP AUTOENCODER BASED FEATURE EXTRACTION

Abstract

Nowadays, smart phones have become an indispensable part of human life. The Android operating system has the highest utilization rate among these devices. Its advanced features allow users to store personal information such as photos, health data, identity information, and bank information. It is the most targeted operating system by malware developers because of its widespread usage and advanced features. In this study, in order to increase the success in the detection of Android malware applications, feature extraction was performed by using deep autoencoders. In the next step, data are classified by Random Forest (RF), K-Nearest Neighbor (K-NN) algorithm and Decision Tree (DT) algorithms. Experimental results showed that feature extraction using deep autoencoders and principal component analysis increased the accuracy of model. According to the analyses made, it has been observed that Random Forest algorithm had the best accuracy with 94.40%.

Keywords

• Security,malware detection,machine learning,auto encoder

DERİN OTOMATİK KODLAYICI TABANLI ÖZELLİK ÇIKARIMI İLE ANDROİD KÖTÜCÜL YAZILIM UYGULAMALARININ TESPİTİ

Abstract

Günümüzde akıllı telefonlar insan hayatının vazgeçilmez bir parçası haline gelmiştir. Android işletim sistemi bu cihazlar arasında en yüksek kullanım oranına sahiptir. Gelişmiş özellikleri sayesinde kullanıcıların fotoğrafları, sağlık verileri, kimlik bilgileri ve banka bilgileri gibi kişisel bilgilerini saklamalarını sağlar. Yaygın kullanımı ve gelişmiş özellikleri nedeniyle kötü amaçlı yazılım geliştiricileri tarafından en çok hedeflenen işletim sistemidir. Bu çalışmada Android kötücül yazılım uygulamalarının tespitinde başarıyı artırmak için öncelikle derin oto kodlayıcı mimarisi kullanılarak özellik çıkarımı yapılmıştır. Bir sonraki aşamada ise makine öğrenmesi yöntemlerinden Rasgele Orman (RO), K-En Yakın Komşu (K-EYK) ve Karar Ağacı (KA) algoritmaları kullanılarak sınıflandırma yapılmıştır. Deneysel sonuçlar derin oto kodlayıcı ve temel bileşen analizi kullanarak özellik çıkarımının başarıyı artırdığını göstermiştir. Yapılan analizlere göre, Rastgele Orman algoritmasının % 94,40 ile en iyi doğruluğa sahip olduğu görülmüştür. 

Keywords

• Güvenlik,kötü amaçlı yazılım algılama,makine öğrenmesi,oto kodlayıcı

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