LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers

Şahin D. Ö., Akleylek S., Kılıç E.

IEEE ACCESS, cilt.10, ss.14246-14259, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1109/access.2022.3146363
  • Dergi Adı: IEEE ACCESS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.14246-14259
  • Anahtar Kelimeler: Malware, Classification algorithms, Machine learning, Linear regression, Smart phones, Feature extraction, Machine learning algorithms, Ensemble learning, linear regression, machine learning, malware analysis, permission-based android malware detection, static analysis, SYSTEM
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

In this study, a framework for Android malware detection based on permissions is presented. This framework uses multiple linear regression methods. Application permissions, which are one of the most critical building blocks in the security of the Android operating system, are extracted through static analysis, and security analyzes of applications are carried out with machine learning techniques. Based on the multiple linear regression techniques, two classifiers are proposed for permission-based Android malware detection. These classifiers are compared on four different datasets with basic machine learning techniques such as support vector machine, k-nearest neighbor, Naive Bayes, and decision trees. In addition, using the bagging method, which is one of the ensemble learning, different classifiers are created, and the classification performance is increased. As a result, remarkable performances are obtained with classification algorithms based on linear regression models without the need for very complex classification algorithms.