【摘要】 基于API(Application Programming Interface)的行为表征是主流安卓恶意应用检测和分类方法的重要环节。然而，安卓API的笼统化发展导致该表征方法面临粗粒度、无法精确表征应用行为的问题。针对该问题，基于程序分析和自然语言处理技术，提出自动化的方法对API参数进行语义分析，将表征粒度从API提升至其参数，实现对应用行为的细粒度表征。实验结果表明该方法可显著提高安卓应用行为表征的精确性，提升恶意应用检测和分类等任务的效果。更多还原

【Abstract】 API-based behavior representation is currently an important part of mainstream Android malware detection and classification. However, due to the rough development of Android APIs, this method faces the problem of coarse granularity and is unable to precisely describe application behaviors. In response to this problem, an automatic API parameter semantic analysis method is proposed, which is based on program analysis and natural language processing technology. It refined the representation granularity from API to its parameters and realized a fine-grained representation of application behaviors. Experimental results show that this method can significantly improve the precision of Android application behavior representation, and enhance the effectiveness of tasks such as malware detection and classification.更多还原