E-ISSN 2814-2195 | ISSN 2736-1667
 
2023, Vol: 4, Issue: 2
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Research Article
Online Published: 11 Nov 2025
 


Android Malware and Benign Ware Application Detection Comparison Using Machine Learning Techniques Based on Permissions and API-calls

Muhammad Musa Lawal, Abubakar Musa Ahmad, Isah Suleiman Bandi And Muhammad Ismaila Mungadi.


Abstract
Android malware is software designed specifically to corrupt, attack, damage, harm, disrupt, sneak, or
gain illegal access to anything of value to the device. Android malware growth has been expanding so
dangerously as a result of the advancement and connivance of developing techniques. There are several
ways for security attacks that affect benign apps due to some techniques that include open source (OS)
for developing Android applications and the permission process. To overcome this, applications from the
entire categories are generally examined using static analysis approach to identify benign and malicious
applications. This paper presents the classification of Android benign and malicious apps based on their
application category, and research proposed an application detection comparison of malware and benign
apps using the requested permissions and Application Program Interface (API) this will help to achieve
the best performance of classification models in identifying malicious apps in the same category in
Android applications. By applying feature extraction and selection from the used datasets, the Naïve
Bayes classifier with 10 random tests for the classes of both "Entertainment" and "Personalisation"
attained a high-level of true positive and low-level of false-positive.

Key words: Android, Permissions, APIs, Static, and Machine Learning


 
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How to Cite this Article
Pubmed Style

Lawal MM, Ahmad AM, . Android Malware and Benign Ware Application Detection Comparison Using Machine Learning Techniques Based on Permissions and API-calls . SJACR. 2023; 4(2): 12-21.

Web Style

Lawal MM, Ahmad AM, . Android Malware and Benign Ware Application Detection Comparison Using Machine Learning Techniques Based on Permissions and API-calls . https://www.sjacrksusta.com/?mno=295976 [Access: November 17, 2025].

AMA (American Medical Association) Style

Lawal MM, Ahmad AM, . Android Malware and Benign Ware Application Detection Comparison Using Machine Learning Techniques Based on Permissions and API-calls . SJACR. 2023; 4(2): 12-21.


Vancouver/ICMJE Style

Lawal MM, Ahmad AM, . Android Malware and Benign Ware Application Detection Comparison Using Machine Learning Techniques Based on Permissions and API-calls . SJACR. (2023), [cited November 17, 2025]; 4(2): 12-21.


Harvard Style

Lawal, M. M., Ahmad, . A. M. & (2023) Android Malware and Benign Ware Application Detection Comparison Using Machine Learning Techniques Based on Permissions and API-calls . SJACR, 4 (2), 12-21.


Turabian Style

Lawal, Muhammad Musa, Abubakar Musa Ahmad, and Isah Suleiman Bandi And Muhammad Ismaila Mungadi. 2023. Android Malware and Benign Ware Application Detection Comparison Using Machine Learning Techniques Based on Permissions and API-calls . Science Journal of Advanced and Cognitive Research, 4 (2), 12-21.


Chicago Style

Lawal, Muhammad Musa, Abubakar Musa Ahmad, and Isah Suleiman Bandi And Muhammad Ismaila Mungadi. "Android Malware and Benign Ware Application Detection Comparison Using Machine Learning Techniques Based on Permissions and API-calls ." Science Journal of Advanced and Cognitive Research 4 (2023), 12-21.


MLA (The Modern Language Association) Style

Lawal, Muhammad Musa, Abubakar Musa Ahmad, and Isah Suleiman Bandi And Muhammad Ismaila Mungadi. "Android Malware and Benign Ware Application Detection Comparison Using Machine Learning Techniques Based on Permissions and API-calls ." Science Journal of Advanced and Cognitive Research 4.2 (2023), 12-21. Print.


APA (American Psychological Association) Style

Lawal, M. M., Ahmad, . A. M. & (2023) Android Malware and Benign Ware Application Detection Comparison Using Machine Learning Techniques Based on Permissions and API-calls . Science Journal of Advanced and Cognitive Research, 4 (2), 12-21.


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