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 stati...
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IEEE
2022-01-01
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Online Access: | https://ieeexplore.ieee.org/document/9694615/ |
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author | Durmus Ozkan Sahin Sedat Akleylek Erdal Kilic |
author_facet | Durmus Ozkan Sahin Sedat Akleylek Erdal Kilic |
author_sort | Durmus Ozkan Sahin |
collection | DOAJ |
description | 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. |
format | Article |
id | doaj-art-c61d96c96b8947c2872fd65e70c1b1d9 |
institution | Kabale University |
issn | 2169-3536 |
language | English |
publishDate | 2022-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
spelling | doaj-art-c61d96c96b8947c2872fd65e70c1b1d92025-02-08T00:00:11ZengIEEEIEEE Access2169-35362022-01-0110142461425910.1109/ACCESS.2022.31463639694615LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based ClassifiersDurmus Ozkan Sahin0https://orcid.org/0000-0002-0831-7825Sedat Akleylek1https://orcid.org/0000-0001-7005-6489Erdal Kilic2https://orcid.org/0000-0003-1585-0991Department of Computer Engineering, Ondokuz Mayıs University, Samsun, TurkeyDepartment of Computer Engineering, Ondokuz Mayıs University, Samsun, TurkeyDepartment of Computer Engineering, Ondokuz Mayıs University, Samsun, TurkeyIn 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.https://ieeexplore.ieee.org/document/9694615/Ensemble learninglinear regressionmachine learningmalware analysispermission-based android malware detectionstatic analysis |
spellingShingle | Durmus Ozkan Sahin Sedat Akleylek Erdal Kilic LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers IEEE Access Ensemble learning linear regression machine learning malware analysis permission-based android malware detection static analysis |
title | LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers |
title_full | LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers |
title_fullStr | LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers |
title_full_unstemmed | LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers |
title_short | LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers |
title_sort | linregdroid detection of android malware using multiple linear regression models based classifiers |
topic | Ensemble learning linear regression machine learning malware analysis permission-based android malware detection static analysis |
url | https://ieeexplore.ieee.org/document/9694615/ |
work_keys_str_mv | AT durmusozkansahin linregdroiddetectionofandroidmalwareusingmultiplelinearregressionmodelsbasedclassifiers AT sedatakleylek linregdroiddetectionofandroidmalwareusingmultiplelinearregressionmodelsbasedclassifiers AT erdalkilic linregdroiddetectionofandroidmalwareusingmultiplelinearregressionmodelsbasedclassifiers |