Generate optimal number of features in mobile malware classification using Venn diagram intersection

Ismail, Najiahtul Syafiqah and Yusof, Robiah and Abdollah, Mohd Faizal (2022) Generate optimal number of features in mobile malware classification using Venn diagram intersection. IJCSNS International Journal of Computer Science and Network Security, 22 (7). pp. 389-396. ISSN 1738-7906

Text IJCSNS-GENERATE OPTIMAL NUMBER OF FEATURES IN MMC USING VENN DIAGRAM INTERSECTION.PDF Download (362kB)

Abstract

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.

Item Type:	Article
Uncontrolled Keywords:	Mobile malware, Classification, Permissions, Intersection technique, Intents
Divisions:	Faculty of Information and Communication Technology
Depositing User:	mr eiisaa ahyead
Date Deposited:	24 Mar 2023 10:52
Last Modified:	24 Mar 2023 10:52
URI:	http://eprints.utem.edu.my/id/eprint/26589
Statistic Details:	View Download Statistic

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