FAM: Featuring Android Malware for Deep Learning-Based Familial Analysis

IEEE Access

Dokyung Song¹  Younghoon Ban²  Sunjun Lee³  Jeong Hyun Yi³  Haehyun Cho³ 

[1] Department of Computer Science, Yonsei University, Seoul, South Korea;School of Software Convergence, Soongsil University, Seoul, South Korea;School of Software, Soongsil University, Seoul, South Korea;

关键词: Malware classification;    deep learning;    Android security;    abstract syntax tree;   

DOI  :  10.1109/ACCESS.2022.3151357

来源: DOAJ

【 摘 要 】

To handle relentlessly emerging Android malware, deep learning has been widely adopted in the research community. Prior work proposed deep learning-based approaches that use different features of malware, and reported a high accuracy in malware detection, i.e., classifying malware from benign applications. However, familial analysis of real-world Android malware has not been extensively studied yet. Familial analysis refers to the process of classifying a given malware into a family (or a set of families), which can greatly accelerate malware analysis as the analysis gives their fine-grained behavioral characteristics. In this work, we shed light on deep learning-based familial analysis by studying different features of Android malware and how effectively they can represent their (malicious) behaviors. We focus on string features of Android malware, namely the Abstract Syntax Trees (AST) of all functions extracted from each malware, which faithfully represent all string features of Android malware. We thoroughly study how different string features, such as how security-sensitive APIs are used in malware, affect the performance of our deep learning-based familial analysis model. A convolutional neural network was trained and tested in various configurations on 28,179 real-world malware dataset appeared in the wild from 2018 to 2020, where each malware has one or more labels assigned based on their behaviors. Our evaluation reveals how different features contribute to the performance of familial analysis. Notably, with all features combined, we were able to produce up to an accuracy of 98% and a micro F1-score of 0.82, a result on par with the state-of-the-art.

【 授权许可】

Unknown