Smartphone Applications, Malware and Data Theft

The growing popularity of smartphone devices has led to development of increasing numbers of applications which have subsequently become targets for malicious authors. Analysing applications in order to identify malicious ones is a current major concern in information security; an additional problem connected with smart-phone applications is that their many advertising libraries can lead to loss of personal information. In this paper, we relate the current methods of detecting malware on smartphone devices and discuss the problems caused by malware as well as advertising.

[1]  John Yearwood,et al.  Performance evaluation of multi-tier ensemble classifiers for phishing websites , 2012 .

[2]  Seong-je Cho,et al.  RGBDroid: A Novel Response-Based Approach to Android Privilege Escalation Attacks , 2012, LEET.

[3]  Christopher Krügel,et al.  A survey on automated dynamic malware-analysis techniques and tools , 2012, CSUR.

[4]  Shashi Shekhar,et al.  AdSplit: Separating Smartphone Advertising from Applications , 2012, USENIX Security Symposium.

[5]  Xuxian Jiang,et al.  Unsafe exposure analysis of mobile in-app advertisements , 2012, WISEC '12.

[6]  Hao Chen,et al.  Investigating User Privacy in Android Ad Libraries , 2012 .

[7]  Muttukrishnan Rajarajan,et al.  An Analysis of Tracking Settings in Blackberry 10 and Windows Phone 8 Smartphones , 2014, ACISP.

[8]  Chamseddine Talhi,et al.  Enhancing Smartphone Malware Detection Performance by Applying Machine Learning Hybrid Classifiers , 2012 .

[9]  Peter Christen,et al.  Data Matching , 2012, Data-Centric Systems and Applications.

[10]  Xuxian Jiang,et al.  DroidChameleon: evaluating Android anti-malware against transformation attacks , 2013, ASIA CCS '13.

[11]  Jules White,et al.  Applying machine learning classifiers to dynamic Android malware detection at scale , 2013, 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC).

[12]  Latifur Khan,et al.  A Machine Learning Approach to Android Malware Detection , 2012, 2012 European Intelligence and Security Informatics Conference.

[13]  Veelasha Moonsamy,et al.  Analysis of malicious and benign android applications , 2012, 2012 32nd International Conference on Distributed Computing Systems Workshops.

[14]  Veelasha Moonsamy,et al.  Towards an understanding of the impact of advertising on data leaks , 2012, Int. J. Secur. Networks.

[15]  J. Kent Information gain and a general measure of correlation , 1983 .

[16]  David A. Wagner,et al.  AdDroid: privilege separation for applications and advertisers in Android , 2012, ASIACCS '12.

[17]  Howard J. Hamilton,et al.  Parametric Algorithms for Mining Share Frequent Itemsets , 2001, Journal of Intelligent Information Systems.

[18]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[19]  Huanguo Zhang,et al.  Research on android malware detection and interception based on behavior monitoring , 2012, Wuhan University Journal of Natural Sciences.

[20]  Lynn Batten,et al.  Zero permission android applications - attacks and defenses , 2012 .

[21]  Tao Zhang,et al.  RobotDroid: A Lightweight Malware Detection Framework On Smartphones , 2012, J. Networks.

[22]  C. Jun,et al.  Performance of some variable selection methods when multicollinearity is present , 2005 .

[23]  Win Zaw,et al.  Permission-Based Android Malware Detection , 2013 .

[24]  Veelasha Moonsamy,et al.  Can Smartphone Users Turn Off Tracking Service Settings? , 2013, MoMM '13.

[25]  Yi-Bin Lu,et al.  Using Multi-Feature and Classifier Ensembles to Improve Malware Detection , 2010 .