Modeling and predicting the dynamics of mobile virus spread affected by human behavior

Viruses and malwares can spread from computer networks to mobile networks with the rapid growth of smart cellpone users. In a mobile network, viruses and malwares can cause privacy leakage, extra charges, remote listening and accessing private short messages and call history logs etc. Furthermore, they can jam wireless servers by sending thousands of spam messages or track user positions via GPS. Because of the potential damages of mobile viruses, it is important for us to design a realistic propagation model to observe and understand the propagation mechanisms of mobile viruses. In this paper, we propose a two-layer model to simulate the propagation process of BT-based and SMS-based viruses in mobile networks. Different from previous work, here we focus on the impacts of human behavior, i.e., human operations and mobility patterns, on virus propagation. Through simulations, we aim to gain some insights into how human behavior affects the dynamics of virus spread in mobile networks.

[1]  Binshan Lin,et al.  Security aspects of mobile phone virus: a critical survey , 2008, Ind. Manag. Data Syst..

[2]  Sajal K. Das,et al.  Deployment-aware modeling of node compromise spread in wireless sensor networks using epidemic theory , 2009, TOSN.

[3]  Julinda Stefa,et al.  SWIM: A Simple Model to Generate Small Mobile Worlds , 2008, IEEE INFOCOM 2009.

[4]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[5]  T. Geisel,et al.  Forecast and control of epidemics in a globalized world. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[6]  William H. Sanders,et al.  Quantifying the Effectiveness of Mobile Phone Virus Response Mechanisms , 2007, 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07).

[7]  Injong Rhee,et al.  On the levy-walk nature of human mobility , 2011, TNET.

[8]  Mads Haahr,et al.  Social network analysis for routing in disconnected delay-tolerant MANETs , 2007, MobiHoc '07.

[9]  Albert-László Barabási,et al.  Understanding the Spreading Patterns of Mobile Phone Viruses , 2009, Science.

[10]  Alessandro Vespignani,et al.  The role of the airline transportation network in the prediction and predictability of global epidemics , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Guanhua Yan,et al.  Modeling Propagation Dynamics of Bluetooth Worms (Extended Version) , 2009, IEEE Transactions on Mobile Computing.

[12]  John F. Morar,et al.  An environment for controlled worm replication and analysis , 2000 .

[13]  Sougata Mukherjea,et al.  Analyzing the Structure and Evolution of Massive Telecom Graphs , 2008, IEEE Transactions on Knowledge and Data Engineering.

[14]  V. Jansen,et al.  Modelling the influence of human behaviour on the spread of infectious diseases: a review , 2010, Journal of The Royal Society Interface.

[15]  Donald F. Towsley,et al.  Modeling and Simulation Study of the Propagation and Defense of Internet E-mail Worms , 2007, IEEE Transactions on Dependable and Secure Computing.

[16]  Ning Zhong,et al.  Network immunization and virus propagation in email networks: experimental evaluation and analysis , 2010, Knowledge and Information Systems.

[17]  Geoffrey M. Voelker,et al.  Defending Mobile Phones from Proximity Malware , 2009, IEEE INFOCOM 2009.

[18]  Fan Zhang,et al.  Stealthy video capturer: a new video-based spyware in 3G smartphones , 2009, WiSec '09.

[19]  Sajal K. Das,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON MOBILE COMPUTING An Epidemic Theoretic Framework for Vulnerability Analysi , 2022 .

[20]  Christos Faloutsos,et al.  Mobile call graphs: beyond power-law and lognormal distributions , 2008, KDD.

[21]  Sajal K. Das,et al.  Modeling node compromise spread in wireless sensor networks using epidemic theory , 2006, 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06).

[22]  Pan Hui,et al.  Impact of Human Mobility on the Design of Opportunistic Forwarding Algorithms , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[23]  Injong Rhee,et al.  SLAW: A New Mobility Model for Human Walks , 2009, IEEE INFOCOM 2009.

[24]  Ahmed Helmy,et al.  Modeling Time-Variant User Mobility in Wireless Mobile Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[25]  Kang G. Shin,et al.  Detecting energy-greedy anomalies and mobile malware variants , 2008, MobiSys '08.

[26]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[27]  Mark E. J. Newman,et al.  Technological Networks and the Spread of Computer Viruses , 2004, Science.

[28]  M. Newman Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Songwu Lu,et al.  Analysis of the Reliability of a Nationwide Short Message Service , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[30]  Sencun Zhu,et al.  A Social Network Based Patching Scheme for Worm Containment in Cellular Networks , 2009, IEEE INFOCOM 2009.

[31]  S. Chong,et al.  SLAW : A Mobility Model for Human Walks , 2009 .

[32]  Sencun Zhu,et al.  A systematic approach for cell-phone worm containment , 2008, WWW.

[33]  Geoffrey M. Voelker,et al.  Can you infect me now?: malware propagation in mobile phone networks , 2007, WORM '07.

[34]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[35]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.