DocumentCode :
1876843
Title :
Cloud-based Android botnet malware detection system
Author :
Jadhav, Suyash ; Dutia, Shobhit ; Calangutkar, Kedarnath ; Tae Oh ; Young Ho Kim ; Joeng Nyeo Kim
Author_Institution :
Dept. of Inf. Sci. & Technol., Rochester Inst. of Technol., Rochester, NY, USA
fYear :
2015
fDate :
1-3 July 2015
Firstpage :
347
Lastpage :
352
Abstract :
Increased use of Android devices and its open source development framework has attracted many digital crime groups to use Android devices as one of the key attack surfaces. Due to the extensive connectivity and multiple sources of network connections, Android devices are most suitable to botnet based malware attacks. The research focuses on developing a cloud-based Android botnet malware detection system. A prototype of the proposed system is deployed which provides a runtime Android malware analysis. The paper explains architectural implementation of the developed system using a botnet detection learning dataset and multi-layered algorithm used to predict botnet family of a particular application.
Keywords :
Android (operating system); cloud computing; invasive software; smart phones; botnet detection learning dataset; cloud-based Android botnet malware detection system; digital crime groups; multilayered algorithm; open source development framework; runtime Android malware analysis; Androids; Classification algorithms; Feature extraction; Humanoid robots; Java; Malware; Servers; Android Sandbox; Android botnet; Android botnet family detection; Android on VirtualBox; Cloud-based malware detection; Vyatta;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Communication Technology (ICACT), 2015 17th International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-8-9968-6504-9
Type :
conf
DOI :
10.1109/ICACT.2015.7224817
Filename :
7224817
Link To Document :
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