A novel permission-based Android malware detection system using feature selection based on linear regression

Şahin D. Ö., Kural O. E., Akleylek S., Kılıç E.

NEURAL COMPUTING & APPLICATIONS, cilt.35, sa.7, ss.4903-4918, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35 Sayı: 7
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s00521-021-05875-1
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Sayfa Sayıları: ss.4903-4918
  • Anahtar Kelimeler: Android malware, Malware detection, Feature selection, Static analysis, Machine learning, Linear regression, CODE
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

With the developments in mobile and wireless technology, mobile devices have become an important part of our lives. While Android is the leading operating system in market share, it is the platform most targeted by attackers. Although many solutions have been proposed in the literature for the detection of Android malware, there is still a need for attribute selection methods to be used in Android malware detection systems. In this study, a machine learning-based malware detection system is proposed to distinguish Android malware from benign applications. At the feature selection stage of the proposed malware detection system, it is aimed to remove unnecessary features by using a linear regression-based feature selection approach. In this way, the dimension of the feature vector is reduced, the training time is decreased, and the classification model can be used in real-time malware detection systems. When the results of the study are examined, the highest 0.961 is obtained according to the F-measure metric by using at least 27 features.