Analysis of Markov Blanket Based Feature Ranking for Android Malware Detection

Abstract

The ubiquity of Android applications in our daily lives has brought forth an indispensable need for robust app security mechanisms. Malware-infested applications not only jeopardize user privacy but also compromise data integrity and overall device security. Detecting and mitigating malicious behavior within Android applications is becoming increasingly challenging due to the high-dimensional nature of the data. Moving forward, employing Machine Learning (ML) techniques to detect malware in Android apps has become the norm. High dimensional feature space poses several formidable challenges, including data scarcity, over fitting, and heightened computational demands. While dimensionality reduction techniques are a potential remedy, they often result in loss of crucial information essential for comprehending and identifying malicious behaviors accurately. Feature ranking based feature subset selection emerges as a promising alternative, as it allows us to retain the original feature space, ensuring precise representation of app behaviors. This article explores various Markov Blanket (MB) based feature ranking techniques, proposes a variant of Grow Shrink (GS) MB technique, GS++ which demonstrates enhanced performance within the context of Android malware detection.