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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware. The most extensively used method for identifying Android malware is signature-based detection. The drawback of this method, however, is that it is unable to detect unknown malware. As a consequence of this problem, machine learning (ML) methods for detecting and classifying malware applications were developed. The goal of conventional ML approaches is to improve classification accuracy. However, owing to imbalanced real-world datasets, the traditional classification algorithms perform poorly in detecting malicious apps. As a result, in this study, we developed a meta-learning approach based on the forest penalizing attribute (FPA) classification algorithm for detecting malware applications. In other words, with this research, we investigated how to improve Android malware detection by applying empirical analysis of FPA and its enhanced variants (Cas_FPA and RoF_FPA). The proposed FPA and its enhanced variants were tested using the Malgenome and Drebin Android malware datasets, which contain features gathered from both static and dynamic Android malware analysis. Furthermore, the findings obtained using the proposed technique were compared with baseline classifiers and existing malware detection methods to validate their effectiveness in detecting malware application families. Based on the findings, FPA outperforms the baseline classifiers and existing ML-based Android malware detection models in dealing with the unbalanced family categorization of Android malware apps, with an accuracy of 98.94% and an area under curve (AUC) value of 0.999. Hence, further development and deployment of FPA-based meta-learners for Android malware detection and other cybersecurity threats is recommended.

Details

Title
Empirical Analysis of Forest Penalizing Attribute and Its Enhanced Variations for Android Malware Detection
Author
Akintola, Abimbola G 1 ; Balogun, Abdullateef O 2   VIAFID ORCID Logo  ; Capretz, Luiz Fernando 3   VIAFID ORCID Logo  ; Mojeed, Hammed A 4   VIAFID ORCID Logo  ; Shuib Basri 5 ; Salihu, Shakirat A 1   VIAFID ORCID Logo  ; Usman-Hamza, Fatima E 1 ; Sadiku, Peter O 1 ; Balogun, Ghaniyyat B 1 ; Alanamu, Zubair O 1 

 Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria; [email protected] (A.G.A.); [email protected] (H.A.M.); [email protected] (S.A.S.); [email protected] (F.E.U.-H.); [email protected] (P.O.S.); [email protected] (G.B.B.); [email protected] (Z.O.A.) 
 Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria; [email protected] (A.G.A.); [email protected] (H.A.M.); [email protected] (S.A.S.); [email protected] (F.E.U.-H.); [email protected] (P.O.S.); [email protected] (G.B.B.); [email protected] (Z.O.A.); Department of Computer and Information Science, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia; [email protected] 
 Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada; [email protected] 
 Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria; [email protected] (A.G.A.); [email protected] (H.A.M.); [email protected] (S.A.S.); [email protected] (F.E.U.-H.); [email protected] (P.O.S.); [email protected] (G.B.B.); [email protected] (Z.O.A.); Department of Technical Informatics and Telecommunications, Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland 
 Department of Computer and Information Science, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia; [email protected] 
First page
4664
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2662926466
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.