Enhancing Malware Detection for Android Apps: Detecting Fine-Granularity Malicious Components

doi:10.1109/ASE56229.2023.00074

	Enhancing Malware Detection for Android Apps: Detecting Fine-Granularity Malicious Components
	Liu, Zhijie1 ; Zhang, Liang Feng1 ; Tang, Yutian 2
	2023-09-15
会议录名称	2023 38TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE)
ISSN	1938-4300
页码	1212-1224
发表状态	已发表
DOI	10.1109/ASE56229.2023.00074
摘要	Existing Android malware detection systems primarily concentrate on detecting malware apps, leaving a gap in the research concerning the detection of malicious components in apps. In this work, we propose a novel approach to detect fine-granularity malicious components for Android apps and build a prototype called AMCDroid. For a given app, AMCDroid first models app behavior to a homogenous graph based on the call graph and code statements of the app. Then, the graph is converted to a statement tree sequence for malware detection through the AST-based Neural Network with Feature Mapping (ASTNNF) model. Finally, if the app is detected as malware, AMCDroid applies fine-granularity malicious component detection (MCD) algorithm which is based on many-objective genetic algorithm to the homogenous graph for detecting malicious component in the app adaptively. We evaluate AMCDroid on 95,134 samples. Compared with the other two state-of-the-art methods in malware detection, AMCDroid gets the highest performance on the test set with 0.9699 F1-Score, and shows better robustness in facing obfuscation. Moreover, AMCDroid is capable of detecting fine-granularity malicious components of (obfuscated) malware apps. Especially, its average F1-Score exceeds another state-of-the-art method by 50%. © 2023 IEEE.
关键词	Android malware app deep learning many-objective genetic algorithm malicious component
会议名称	38th IEEE/ACM International Conference on Automated Software Engineering, ASE 2023
会议地点	Luxembourg, Luxembourg
会议日期	11-15 Sept. 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20235015191698
EI主题词	Genetic algorithms
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 723 Computer Software, Data Handling and Applications ; 723.2 Data Processing and Image Processing ; 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory
原始文献类型	Conference article (CA)
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/347934
专题	信息科学与技术学院信息科学与技术学院_PI研究组_张良峰组信息科学与技术学院_硕士生
通讯作者	Tang, Yutian
作者单位	1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China; 2.School of Computing Science, University of Glasgow, United Kingdom
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Liu, Zhijie,Zhang, Liang Feng,Tang, Yutian. Enhancing Malware Detection for Android Apps: Detecting Fine-Granularity Malicious Components[C]:Institute of Electrical and Electronics Engineers Inc.,2023:1212-1224.