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Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection
Abstract
Due to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for detecting malware apps was created. Conventional ML methods are concerned with increasing classification accuracy. However, the standard classification method performs poorly in recognizing malware applications due to the unbalanced real-world datasets. In this study, an empirical analysis of the detection performance of ML methods in the presence of class imbalance is conducted. Specifically, eleven (11) ML methods with diverse computational complexities were investigated. Also, the synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) are deployed to address the class imbalance in the Android malware datasets. The experimented ML methods are tested using the Malgenome and Drebin Android malware datasets that contain features gathered from both static and dynamic malware approaches. According to the experimental findings, the performance of each experimented ML method varies across the datasets. Moreover, the presence of class imbalance deteriorated the performance of the ML methods as their performances were amplified with the deployment of data sampling methods (SMOTE and RUS) used to alleviate the class imbalance problem. Besides, ML models with SMOTE technique are superior to ML models based on the RUS method. It is therefore recommended to address the inherent class imbalance problem in Android Malware detection
Citations
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0
Web of Science
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8
Scopus
Authors (8)
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Abimbola G. Akintola
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Abdullateef O. Balogun
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Fatima Usman-Hamza
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Shakirat A. Salihu
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Kayode S. Adewole
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Ghaniyyat B. Balogun
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Peter O. Sadiku
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3991/ijim.v16i10.29687
- License
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Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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International Journal of Interactive Mobile Technologies
no. 16,
pages 140 - 162,
ISSN: - Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Akintola A. G., Balogun A. O., Mojeed H., Usman-Hamza F., Salihu S. A., Adewole K. S., Balogun G. B., Sadiku P. O.: Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection// International Journal of Interactive Mobile Technologies -Vol. 16,iss. 10 (2022), s.140-162
- DOI:
- Digital Object Identifier (open in new tab) 10.3991/ijim.v16i10.29687
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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