PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection.

ABSTRACT: The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting

SUBMITTER: Mahindru A 

PROVIDER: S-EPMC11636933 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature