计算机科学
恶意软件
Android恶意软件
Android(操作系统)
特征提取
人工智能
机器学习
静态分析
数据挖掘
计算机安全
操作系统
程序设计语言
作者
Yucai Song,Chen Yang,Bo Lang,Hongyu Liu,Shaojie Chen
标识
DOI:10.1007/978-3-030-24907-6_29
摘要
Nowadays, the security risks brought by Android malwares are increasing. Machine learning is considered as a potential solution for promoting the performance of malware detection. For machine learning based Android malware detection, feature extraction plays a key role. Thinking the source codes of applications are comparable with text documents, we propose a new Android malware detection method based on the topic model which is an effective technique in text feature extraction. Our method regards the decompiled codes of an application as a text document, and the topic model is used to mine the potential topics in the codes which can reflect the semantic feature of the application. The experimental results demonstrate that, our approach performs better than the state-of-the-art methods. Also, our method mines the features in the application files automatically without manually design, and therefore overcomes the limitation in present methods which relies on experts' prior knowledge.
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