DocumentCode :
186092
Title :
Android Malware Detection Using Parallel Machine Learning Classifiers
Author :
Yerima, Suleiman Y. ; Sezer, Sakir ; Muttik, Igor
Author_Institution :
Centre for Secure Inf. Technol. (CSIT), Queen´s Univ., Belfast, UK
fYear :
2014
fDate :
10-12 Sept. 2014
Firstpage :
37
Lastpage :
42
Abstract :
Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.
Keywords :
Android (operating system); invasive software; learning (artificial intelligence); mobile computing; parallel processing; Android malware detection; mobile malware; mobile smart devices market; open platform; parallel machine learning classifiers; Accuracy; Androids; Classification algorithms; Feature extraction; Humanoid robots; Malware; Training; Android; data mining; machine learning; malware detection; mobile security; parallel classifiers; static analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Next Generation Mobile Apps, Services and Technologies (NGMAST), 2014 Eighth International Conference on
Conference_Location :
Oxford
Print_ISBN :
978-1-4799-5072-0
Type :
conf
DOI :
10.1109/NGMAST.2014.23
Filename :
6982888
Link To Document :
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