Malware Detection in Android OS using Machine Learning Techniques

Maad M. Mıjwıl

Malware Detection in Android OS using Machine Learning Techniques

Abstract

Malware is a software that is created to distort or obstruct computer or mobile applications, gather sensitive information or execute malicious actions. These malicious activities include increasing access through personal information, stealing this valuable information from the system, spying on a user’s activity, and displaying unwanted ads. Nowadays, mobile devices have become an essential part of our times, therefore we always need active algorithms for malware detection. In this paper, supervised machine learning techniques (SMLTs):Random Forest (RF), support vector machine(SVM), Naïve Bayes (NB)and decision tree(ID3) are applied in the detection of malware on Android OS and their performances have been compared. These techniques rely on Java APIs as well as the permissions required by employment as features to generalize their behavior and differentiate whether it is benign or malicious. The experimentation of results proves that RF has the highest performance with an accuracy rate of 96.2%

Keywords

Machine learning techniques , Malware Detection , Android OS , Java applications

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Yapay Yaşam ve Karmaşık Uyarlanabilir Sistemler

Bölüm

Araştırma Makalesi

Yazarlar

Maad M. Mıjwıl
Türkiye

Yayımlanma Tarihi

31 Aralık 2020

Gönderilme Tarihi

24 Temmuz 2020

Kabul Tarihi

22 Aralık 2020

Yayımlandığı Sayı

Yıl 2020 Cilt: 3 Sayı: 2

IZ

https://izlik.org/JA25MY87FN

IEEE

[1]M. M. Mıjwıl, “Malware Detection in Android OS using Machine Learning Techniques”, DataSCI, c. 3, sy 2, ss. 5–9, Ara. 2020, [çevrimiçi]. Erişim adresi: https://izlik.org/JA25MY87FN