ملخص الدراسة:

Mobile computing is an important field in information technology, because of widely using mobile devises and mobile applications. Clustering is a substantial task gives good results with information retrieval (IR); it aims to put automatically similar applications in one cluster. In this paper, we present an unsupervised machine learning based system for automatic malware detection. We will evaluate clustering technique in Android applications, which gives indication of applying clustering techniques in malware detection of Android applications, to use Machine Learning techniques in automatic detection of malicious applications in Android market. An evaluation will be provided by using F-Measure in clustering two categories of Android applications: Business, and Tools. 18,174 Android’s application files will be used in clustering and evaluation. Features will be extracted from Android’s XML-files, which contains permissions requested by applications. The results give indication of using unsupervised machine learning techniques in malware detection of mobile applications using couple of application information's and xml AndroidManifest files.

توثيق المرجعي (APA)

AbuSamra, Aiman A, Yim, Kangbin,& Ghanem, Osama A. (2013). Analysis of Clustering Technique in Android Malware Detection. 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Institute of Electrical and Electronics Engineers (IEEE). 27233