Mobile Networks and Applications

, Volume 18, Issue 6, pp 908–922 | Cite as

Enhancing Efficiency of Node Compromise Attacks in Vehicular Ad-hoc Networks Using Connected Dominating Set

  • Chi Lin
  • Guowei Wu
  • Feng Xia
  • Lin Yao


In the node compromise attack, the adversary physically captures nodes and extracts the cryptographic keys from the memories, which destroys the security, reliability and confidentiality of the networks. Due to the dynamical network topology, designing an efficient node compromise attack algorithm is challenging, because it is difficult to model the attack or to enhance the attacking efficiency. In this paper, a general algorithm for modeling the node compromise attack in VANET is proposed, which promotes the attacking efficiency by destroying the network backbone. The backbone is constructed using the connected dominating set of the network, which has relevant to the intermeeting time between the vehicles. Then two attacking algorithms are proposed based on the general model, which destroy the network in a centralized and distributed version while maximizing the destructiveness. Simulations are conducted to show the advantages of our scheme. Simulation results reveal that our scheme enhances the attacking efficiency in different mobility models and different applications, which is suitable for modeling the node compromise attack in VANET. At last, discussions are presented to the illustrate the influences of the characteristics to the attacking efficiency with respect to vehicle speed, communication range and key sharing probability.


VANET Node compromise attack Connected dominating set Attack modeling Attack efficiency 



This research is sponsored in part by the National Natural Science Foundation of China and the Fundamental Research Funds for the Central Universities (contract/grant number: No. 61173179 and No.61202441). This research is also sponsored in part supported by the Fundamental Research Funds for the Central Universities (No. DUT13JS10).


  1. 1.
    Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422CrossRefGoogle Scholar
  2. 2.
    Almahorg K, Basir O (2008) Simulation-based performance comparison of vanets backbone formation algorithms. In: Proceedings of the 2008 12th IEEE/ACM international symposium on distributed simulation and real-time applications. IEEE Computer Society, pp 236–242Google Scholar
  3. 3.
    Aziz A, Sanwal K, Singhal V, Brayton R (2000) Model-checking continuous-time markov chains. ACM Trans Comput Logic (TOCL) 1(1):162–170MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bettstetter C, Hartenstein H, Pérez-Costa X (2004) Stochastic properties of the random waypoint mobility model. Wirel Netw 10(5):555–567CrossRefGoogle Scholar
  5. 5.
    Bonaci T, Bushnell L, Poovendran R (2010) Node capture attacks in wireless sensor networks: a system theoretic approach. In: 2010 49th IEEE conference on decision and control (CDC). IEEE, pp 6765–6772Google Scholar
  6. 6.
    Bonaci T, Bushnell L, Poovendran R (2010) Probabilistic analysis of covering and compromise in node capture attacks. Network Security Lab (NSL), Seattle. Techical Report 1Google Scholar
  7. 7.
    Chan H, Perrig A, Song D (2003) Random key predistribution schemes for sensor networks. In: Proceedings. 2003 symposium on security and privacy, 2003. IEEE, pp 97–213Google Scholar
  8. 8.
    Chan K, Fekri F (2007) Node compromise attacks and network connectivity. In: Defense and security symposium, pp 65,780W–65,780W. International Society for Optics and PhotonicsGoogle Scholar
  9. 9.
    Chi Lin GW (2013) Enhancing the attacking efficiency of the node capture attack in wsn: a matrix approach. J SupercomputGoogle Scholar
  10. 10.
    Dai F, Wu J (2004) An extended localized algorithm for connected dominating set formation in ad hoc wireless networks. IEEE Trans Parallel Distrib Syst 15(10):908–920CrossRefGoogle Scholar
  11. 11.
    De P, Liu Y, Das SK (2006) Modeling node compromise spread in wireless sensor networks using epidemic theory. In: Proceedings of the 2006 international symposium on on world of wireless, mobile and multimedia networks. IEEE Computer Society, pp 237–243Google Scholar
  12. 12.
    De P, Liu Y, Das SK (2009) Deployment-aware modeling of node compromise spread in wireless sensor networks using epidemic theory. ACM Trans Sens Netw (TOSN) 5(3):23–50Google Scholar
  13. 13.
    Ergun M, Levi A, Savas E (2011) Increasing resiliency in multi-phase wireless sensor networks: generationwise key predistribution approach. Comput J 54(4):602–616CrossRefGoogle Scholar
  14. 14.
    Fasolo E, Zanella A, Zorzi M (2006) An effective broadcast scheme for alert message propagation in vehicular ad hoc networks. In: IEEE international conference on communications, 2006. ICC’06, vol 9. IEEE, pp 3960–3965Google Scholar
  15. 15.
    Felice MD, Bedogni L, Bononi L (2012) Group communication on highways: an evaluation study of geocast protocols and applications. Ad Hoc Netw 11(7):818–832Google Scholar
  16. 16.
    Golle P, Greene D, Staddon J (2004) Detecting and correcting malicious data in vanets. In: Proceedings of the 1st ACM international workshop on vehicular ad hoc networks. ACM, pp 29–37Google Scholar
  17. 17.
    Guette G, Ducourthial B (2007) On the sybil attack detection in vanet. In: IEEE internatonal conference on mobile adhoc and sensor systems, 2007. MASS 2007. IEEE, pp 1–6Google Scholar
  18. 18.
    Haerri J, Filali F, Bonnet C (2006) Performance comparison of aodv and olsr in vanets urban environments under realistic mobility patterns. In: Proc. of 5th IFIP mediterranean ad-hoc networking workshop (Med-Hoc-Net-2006), LipariGoogle Scholar
  19. 19.
    Hao Y, Cheng Y, Ren K (2008) Distributed key management with protection against rsu compromise in group signature based vanets. In: Global telecommunications conference, 2008. IEEE GLOBECOM 2008. IEEE, pp 1–5Google Scholar
  20. 20.
    Hasbullah H, Ahmed Soomro I, Ab Manan Jl (2010) Denial of service (dos) attack and its possible solutions in vanet. World Acad Sci Eng Technol (WASET) 65:411–415Google Scholar
  21. 21.
    Husain A, Kumar B, Doegar A (2011) Performance evaluation of routing protocols in vehicular ad hoc networks. Int J Internet Protocol Technol 6(1):38–45CrossRefGoogle Scholar
  22. 22.
    Isaac JT, Zeadally S, Cámara JS (2010) Security attacks and solutions for vehicular ad hoc networks. IET Commun 4(7):894–903zbMATHMathSciNetCrossRefGoogle Scholar
  23. 23.
    Karnadi FK, Mo ZH, Lan KC (2007) Rapid generation of realistic mobility models for vanet. In: IEEE Wireless communications and networking conference, 2007. WCNC 2007. IEEE, pp 2506–2511Google Scholar
  24. 24.
    Khairnar VD, Pradhan S (2011) Mobility models for vehicular ad-hoc network simulation. In: 2011 IEEE symposium on computers & informatics (ISCI). IEEE, pp 460–465Google Scholar
  25. 25.
    Kim DS, Suh YK, Park JS (2007) Toward assessing vulnerability and risk of sensor networks under node compromise. In: 2007 international conference on computational intelligence and security. IEEE, pp 740–744Google Scholar
  26. 26.
    Laurendeau C, Barbeau M (2006) Threats to security in dsrc/wave. In: Ad-hoc, mobile, and wireless networks. Springer, pp 266–279Google Scholar
  27. 27.
    Leinmuller T, Schmidt RK, Schoch E, Held A, Schafer G (2008) Modeling roadside attacker behavior in vanets. In: 2008 IEEE GLOBECOM workshops. IEEE, pp 1–10Google Scholar
  28. 28.
    Lin X, Lu R, Zhang C, Zhu H, Ho PH, Shen X (2008) Security in vehicular ad hoc networks. IEEE Commun Mag 46(4):88–95CrossRefGoogle Scholar
  29. 29.
    Lo NW, Tsai HC (2007) Illusion attack on vanet applications-a message plausibility problem. In: 2007 IEEE Globecom Workshops. IEEE, pp 1–8Google Scholar
  30. 30.
    Martinez FJ, Toh CK, Cano JC, Calafate CT, Manzoni P (2011) A survey and comparative study of simulators for vehicular ad hoc networks (vanets). Wirel Commun Mob Comput 11(7):813–828CrossRefGoogle Scholar
  31. 31.
    Mishra AK, Turuk AK (2011) Adversary information gathering model for node capture attack in wireless sensor networks. In: 2011 international conference on devices and communications (ICDeCom). IEEE, pp 1–5Google Scholar
  32. 32.
    Saha AK, Johnson DB (2004) Modeling mobility for vehicular ad-hoc networks. In: Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks. ACM, pp 91–92Google Scholar
  33. 33.
    Sampigethaya K, Li M, Huang L, Poovendran R (2007) Amoeba: robust location privacy scheme for vanet. IEEE J Select Areas Commun 25(8):1569–1589CrossRefGoogle Scholar
  34. 34.
    Samuel H, Zhuang W, Preiss B (2009) Dtn based dominating set routing for manet in heterogeneous wireless networking. Mob Netw Appl 14(2):154–164CrossRefGoogle Scholar
  35. 35.
    Tague P, Poovendran R (2007) Modeling adaptive node capture attacks in multi-hop wireless networks. Ad Hoc Netw 5(6):801–814CrossRefGoogle Scholar
  36. 36.
    Tague P, Poovendran R (2008) Modeling node capture attacks in wireless sensor networks. In: 2008 46th annual Allerton conference on communication, control, and computing. IEEE, pp 1221–1224Google Scholar
  37. 37.
    Tague P, Slater D, Rogers J, Poovendran R (2008) Vulnerability of network traffic under node capture attacks using circuit theoretic analysis. In: IEEE INFOCOM 2008. The 27th conference on computer communications. IEEE, pp 161–165Google Scholar
  38. 38.
    Tague P, Slater D, Rogers J, Poovendran R (2009) Evaluating the vulnerability of network traffic using joint security and routing analysis. IEEE Trans Depend Secure Comput 6(2):111–123CrossRefGoogle Scholar
  39. 39.
    Tomandl A, Scheuer F, Federrath H (2012) Simulation-based evaluation of techniques for privacy protection in vanets. In: 2012 IEEE 8th international conference on wireless and mobile computing, networking and communications (WiMob). IEEE, pp 165–172Google Scholar
  40. 40.
    Wan PJ, Alzoubi KM, Frieder O (2002) Distributed construction of connected dominating set in wireless ad hoc networks. In: INFOCOM 2002. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, vol. 3, pp. 1597–1604. IEEEGoogle Scholar
  41. 41.
    Wu G, Chen X, Obaidat MS, Lin C (2012) A high efficient node capture attack algorithm in wireless sensor network based on route minimum key set. Secur Commun NetwGoogle Scholar
  42. 42.
    Yan G, Olariu S, Weigle MC (2008) Providing vanet security through active position detection. Comput Commun 31(12):2883–2897CrossRefGoogle Scholar
  43. 43.
    Yang S, Wu J, Dai F (2008) Efficient directional network backbone construction in mobile ad hoc networks. IEEE Trans Parallel Distrib Syst 19(12):1601–1613CrossRefGoogle Scholar
  44. 44.
    Zhou L, Haas ZJ (1999) Securing ad hoc networks. IEEE Netw 13(6):24–30CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Software TechnologyDalian University of TechnologyDalianChina

Personalised recommendations