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

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.

Automated summary

This study presents a framework for Android malware detection based on permissions, using multiple linear regression methods. Application permissions, critical for the security of the Android operating system, are extracted through static analysis, and machine learning techniques are employed for security analysis. Two classifiers are proposed for permission-based Android malware detection, which are compared with basic machine learning techniques on different datasets. The bagging method is utilized to increase classification performance. The results show remarkable performances with classification algorithms based on linear regression models without the need for complex algorithms.