Manilyzer: automated android malware detection through manifest analysis
S Feldman, D Stadther, B Wang - 2014 IEEE 11th International …, 2014 - ieeexplore.ieee.org
As the world's most popular mobile operating system, Google's Android OS is the principal
target of an ever increasing mobile malware threat. To counter this emerging menace, many …
target of an ever increasing mobile malware threat. To counter this emerging menace, many …
Machine learning-based malware detection for Android applications: History matters!
Machine Learning-based malware detection is a promis-ing scalable method for identifying
suspicious applica-tions. In particular, in today's mobile computing realm where thousands …
suspicious applica-tions. In particular, in today's mobile computing realm where thousands …
[PDF][PDF] 运营商恶意软件防护体系与关键技术研究
陈涛， 高鹏， 杜雪涛， 薛姗， 杨满智 - 电信科学, 2014 - infocomm-journal.com
运营商恶意软件防护体系与关键技术研究* Page 1 电信科学2014 年第1 期 运营商恶意软件防护体系
与关键技术研究* 陈涛1,高鹏1,杜雪涛1,薛姗1,杨满智2 (1.中国移动通信集团设计院有限公司北京 …
与关键技术研究* 陈涛1,高鹏1,杜雪涛1,薛姗1,杨满智2 (1.中国移动通信集团设计院有限公司北京 …
[PDF][PDF] Identifying Android malware using machine learning based upon both static and dynamic features
P Topark-Ngarm - 2014 - ecs.wgtn.ac.nz
A recent report showed that more than half (51.6%) of total phone shipments were
smartphones. These devices are as powerful as laptop computers from only a few years ago …
smartphones. These devices are as powerful as laptop computers from only a few years ago …
[PDF][PDF] Identifying Android malware using dynamically obtained
The constant evolution of mobile devices' resources and features turned ordinary phones
into powerful and portable computers, leading their users to perform payments, store …
into powerful and portable computers, leading their users to perform payments, store …
[引用][C] Design and Implementation of Cloud Based DRM Framework on Android Platform
MKD Nakhale, SP Karmore