A data collection approach for Mobile Botnet analysis and detection

Recently, MoBots or Mobile Botnets have become one of the most critical challenges in mobile communication and cyber security. The integration of Mobile devices with the Internet along with enhanced features and capabilities has made them an environment of interest for cyber criminals. Therefore, the spread of sophisticated malware such as Botnets has significantly increased in mobile devices and networks. On the other hand, the Bots and Botnets are newly migrated to mobile devices and have not been fully explored yet. Thus, the efficiency of current security solutions is highly limited due to the lack of available Mobile Botnet datasets and samples. As a result providing a valid dataset to analyse and understand the Mobile botnets has become a crucial issue in mobile security and privacy. In this paper we present an overview of the current available data set and samples and we discuss their advantages and disadvantages. We also propose a model to implement a mobile Botnet test bed to collect data for further analysis.

[1]  D. Gática-Pérez,et al.  Towards rich mobile phone datasets: Lausanne data collection campaign , 2010 .

[2]  Yajin Zhou,et al.  Dissecting Android Malware: Characterization and Evolution , 2012, 2012 IEEE Symposium on Security and Privacy.

[3]  Sonal Mohite A Survey on mobile malware: War without end , 2014 .

[4]  Imad Aad,et al.  The Mobile Data Challenge: Big Data for Mobile Computing Research , 2012 .

[5]  Simin Nadjm-Tehrani,et al.  Crowdroid: behavior-based malware detection system for Android , 2011, SPSM '11.

[6]  M. Eslahi,et al.  Bots and botnets: An overview of characteristics, detection and challenges , 2012, 2012 IEEE International Conference on Control System, Computing and Engineering.

[7]  Hein S. Venter,et al.  Mobile Botnet Detection Using Network Forensics , 2010, FIS.

[8]  Jingyu Hua,et al.  Botnet command and control based on Short Message Service and human mobility , 2013, Comput. Networks.

[9]  Bill Morrow,et al.  BYOD security challenges: control and protect your most sensitive data , 2012, Netw. Secur..

[10]  Daniele Sgandurra,et al.  A Survey on Security for Mobile Devices , 2013, IEEE Communications Surveys & Tutorials.

[11]  Arun Lakhotia,et al.  DroidLegacy: Automated Familial Classification of Android Malware , 2014, PPREW'14.

[12]  D. Barroso,et al.  Botnets – The Silent Threat , 2007 .

[13]  Neal Leavitt,et al.  Mobile Security: Finally a Serious Problem? , 2011, Computer.

[14]  Jan Kok,et al.  Analysis of the BotNet Ecosystem , 2011, CTTE.

[15]  Dong Zhou,et al.  Translation techniques in cross-language information retrieval , 2012, CSUR.

[16]  Zameshkumar J. Balhare,et al.  A Study on Security for Mobile Devices , 2014 .

[17]  M. Eslahi,et al.  MoBots: A new generation of botnets on mobile devices and networks , 2012, 2012 International Symposium on Computer Applications and Industrial Electronics (ISCAIE).

[18]  Zhen Li,et al.  Portfolio optimization of computer and mobile botnets , 2013, International Journal of Information Security.

[19]  Maryam Var Naseri,et al.  BYOD: Current state and security challenges , 2014, 2014 IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE).

[20]  Alessandro Armando,et al.  Bring your own device, securely , 2013, SAC '13.

[21]  N. M. Tahir,et al.  An efficient false alarm reduction approach in HTTP-based botnet detection , 2013, 2013 IEEE Symposium on Computers & Informatics (ISCI).

[22]  Juan E. Tapiador,et al.  Dendroid: A text mining approach to analyzing and classifying code structures in Android malware families , 2014, Expert Syst. Appl..

[23]  Elisa Bertino,et al.  Detecting mobile malware threats to homeland security through static analysis , 2014, J. Netw. Comput. Appl..

[24]  Hee Beng Kuan Tan,et al.  Detection of Mobile Malware in the Wild , 2012, Computer.

[25]  Jong Kim,et al.  Punobot: Mobile Botnet Using Push Notification Service in Android , 2013, WISA.

[26]  Gianluca Dini,et al.  Evaluating the Trust of Android Applications through an Adaptive and Distributed Multi-criteria Approach , 2013, 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications.

[27]  Ronaldo M. Salles,et al.  Botnets: A survey , 2013, Comput. Networks.

[28]  Hormazd Romer,et al.  Best practices for BYOD security , 2014 .

[29]  Lior Rokach,et al.  Mobile malware detection through analysis of deviations in application network behavior , 2014, Comput. Secur..

[30]  Yajin Zhou,et al.  A Survey of Android Malware , 2013 .

[31]  Gabriel Maciá-Fernández,et al.  Survey and taxonomy of botnet research through life-cycle , 2013, CSUR.