A Proactive and Deceptive Perspective for Role Detection and Concealment in Wireless Networks

  • Zhuo LuEmail author
  • Cliff Wang
  • Mingkui Wei


In many wireless networks (e.g., tactical military networks), the one-to-multiple communication model is pervasive due to commanding and control requirements in mission operations. In these networks, the roles of nodes are non-homogeneous; i.e., they are not equally important. This, however, opens a door for an adversary to target important nodes in the network by identifying their roles. In this chapter, we focus on investigating an important open question: how to detect and conceal the roles of nodes in wireless networks? Answers to this question are of essential importance to understand how to identify critical roles and prevent them from being the primary targets. We demonstrate via analysis and simulations that it is feasible and even accurate to identify critical roles of nodes by looking at network traffic patterns. To provide countermeasures against role detection, we propose role concealment methods based on proactive and deceptive network strategies. We use simulations to evaluate the effectiveness and costs of the role concealment methods.


  1. 1.
    Bar-Noy A, Cirincione G, Govindan R, Krishnamurthy S, LaPorta T, Mohapatra P, Neely M, Yener A (2011) Quality-of-information aware networking for tactical military networks. In: Proc. of IEEE IEEE International Conference on Pervasive Computing and Communications (PERCOM) Workshops, pp 2–7Google Scholar
  2. 2.
    Bu T, Duffield N, Presti FL, Towsley D (2002) Network tomography on general topologies. In: Proc. of ACM SIGMETRICSCrossRefGoogle Scholar
  3. 3.
    Candes E, Romberg J, Tao T (2005) Stable signal recovery from incomplete and inaccurate information. Communications on Pure and Applied Mathematics pp 1207–1233Google Scholar
  4. 4.
    Candes E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Information Theory 52:489–509MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Castro R, Coates M, Liang G, Nowak R, Yu B (2004) Network tomography: Recent developments. Statistical Science 19:499–517MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Chen A, Cao J, Bu T (2010) Network tomography: Identifiability and fourier domain estimation. IEEE Trans Signal Processing 58:6029–6039MathSciNetCrossRefGoogle Scholar
  7. 7.
    Elmasry GF (2010) A comparative review of commercial vs. tactical wireless networks. IEEE Communications Magazine 48(10):54–59CrossRefGoogle Scholar
  8. 8.
    Elmasry GF, McCann CJ, Welsh R (2005) Partitioning QoS management for secure tactical wireless ad hoc networks. IEEE Communications Magazine 43(11):116–123CrossRefGoogle Scholar
  9. 9.
    Friedlander MP, Saunders MA (2008) Active-set methods for basis pursuit. In: Proc. of West Coast Optimization Meeting (WCOM)Google Scholar
  10. 10.
    Horton JD, Lopez-Ortiz A (2003) On the number of distributed measurement points for network tomography. In: Proc. of ACM SIGCOMM Internet Measurement Conference (IMC), pp 204–209Google Scholar
  11. 11.
    Kidston D, Shi M (2012) A multicast routing technique for tactical networks. In: Proc. of IEEE Conference on Military Communications (MILCOM), pp 1–6Google Scholar
  12. 12.
    Kunz T, Li L (2010) Broadcasting in multihop mobile tactical networks: To network code or not. In: Proc. of The International Wireless Communications and Mobile Computing Conference (IWCMC), pp 676–680Google Scholar
  13. 13.
    Kunz T, Li L (2014) Robust broadcasting in tactical networks using network coding. In: Proc. of IEEE Conference on Military Communications (MILCOM), pp 1213–1222Google Scholar
  14. 14.
    Lee SH, Lee S, Song H, Lee HS (2009) Wireless sensor network design for tactical military applications: remote large-scale environments. In: Proc. of IEEE Conference on Military Communications (MILCOM), pp 1–7Google Scholar
  15. 15.
    Lu Z, Wang C (Apr. 2015) Network anti-inference: A fundamental perspective on proactive strategies to counter flow inference. In: Proc. of IEEE International Conference on Computer Communications (INFOCOM)Google Scholar
  16. 16.
    Lu Z, Wang C, Wei M (Oct. 2015) On detection and concealment of critical roles in tactical wireless networks. In: Proc. of IEEE Conference on Military Communications (MILCOM)Google Scholar
  17. 17.
    Penrose M (2003) Random Geometric Graphs. Oxford Univ. PressCrossRefzbMATHGoogle Scholar
  18. 18.
    Soule A, Lakhina A, Taft N, Papagiannaki K, Salamatian K, Nucci A, Crovella M, Diot C (2005) Traffic matrices: Balancing measurements, inference and modeling. In: Proc. of ACM SIGMETRICSCrossRefGoogle Scholar
  19. 19.
    Yao H, Jaggi S, Chen M (2010) Network coding tomography for network failures. In: Proc. of IEEE International Conference on Computer Communications (INFOCOM)Google Scholar
  20. 20.
    Zhao Q, Ge Z, Wang J, Xu J (2006) Robust traffic matrix estimation with imperfect information: Making use of multiple data sources. In: Proc. of ACM SIGMETRICS, pp 133–144Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.The University of MemphisMemphisUSA
  2. 2.Army Research OfficeDurhamUSA
  3. 3.North Carolina State UniversityRaleighUSA

Personalised recommendations