@inproceedings{218b32af6f3c4ea8bc55407246ba15e2,
title = "Android malware detection method based on function call graphs",
abstract = "With the rapid development of mobile Internet, mobile devices have been widely used in people{\textquoteright}s daily life, which has made mobile platforms a prime target for malware attack. In this paper we study on Android malware detection method. We propose the method how to extract the structural features of android application from its function call graph, and then use the structure features to build classifier to classify malware. The experiment results show that structural features can effectively improve the performance of malware detection methods.",
keywords = "Android malware, Machine learning, Static detection",
author = "Yuxin Ding and Siyi Zhu and Xiaoling Xia",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 23rd International Conference on Neural Information Processing, ICONIP 2016 ; Conference date: 16-10-2016 Through 21-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46681-1\_9",
language = "英语",
isbn = "9783319466804",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "70--77",
editor = "Kazushi Ikeda and Minho Lee and Akira Hirose and Seiichi Ozawa and Kenji Doya and Derong Liu",
booktitle = "Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings",
address = "德国",
}