Abstract

The network has become essential to our daily life. With the increase in dependence, challenges to the normal operation of the network bear ever more severe consequences. Challenges include malicious attacks, misconfigurations, faults, and operational overloads. Understanding challenges is needed to build resilience mechanism. A crucial part of resilience strategy involves real-time detection of challenges, followed by identification to initiate appropriate remediation. We observe that the state-of-art to challenge detection is insufficient. Our goal is to advocate a new autonomic, distributed challenge detection approach. In this paper, we present a resilient distributed system to identify the challenges that have severe impact on the wired and wireless mesh network (WMN). Our design shows how a challenge (malicious attack) is handled initially by lightweight network monitoring, then progressively applying more heavyweight analysis in order to identify the challenge. Non-malicious challenges could also be simulated by our network failure module. Furthermore, WMNs are an interesting domain to consider network resilience. Automatic detection and mitigation is a desirable property of a resilient WMN. We present guidelines to address the challenge of channel interferences in the WMN. The feasibility of our framework is demonstrated through experiment. We conclude that our proof-of-concept case study has provided valuable insight into resilient networks, which will be useful for further research.

Keywords

Anomaly Detection Wireless Mesh Network Malicious Attack Network Failure Background Traffic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Doerr, C., Omic, J., et al.: Resilience and Survivability for future networking: framework, mechanisms, and experimental evaluation, ResumeNet Deliverable D2.1b (2010) Google Scholar
  3. 3.
    Smith, P., Fry, M., et al.: Resilience and Survivability for future networking: framework, mechanisms, and experimental evaluation, ResumeNet Deliverable D2.2a (2010) Google Scholar
  4. 4.
    Jung, J., Paxson, V., Berger, A., Balakrishnan, H.: Fast portscan detection using sequential hypothesis testing, pp. 211–225. IEEE, Los Alamitos (2004)Google Scholar
  5. 5.
    Wuhib, F., Stadler, R.: Decentralised Service-Level Monitoring Using Network Threshold Alerts. IEEE Communications Magazine, 44 (2006) Google Scholar
  6. 6.
    Jackson, A.W., Milliken, W., Santivanez, C.a., Condell, M., Strayer, W.T.: A Topological Analysis of Monitor Placement, pp. 169–178. IEEE, Los Alamitos (2007)Google Scholar
  7. 7.
    Fry, M., Fischer, M., Karaliopoulos, M., Smith, P., Hutchison, D.: Challenge identification for network resilience. IEEE, Los Alamitos (2010)CrossRefGoogle Scholar
  8. 8.
    Peng, T., Leckie, C., Ramamohanarao, K.: Survey of Network-Based Defense Mechanisms Countering the DoS and DDoS Problems. ACM Computing Surveys 1, 39 (2007)Google Scholar
  9. 9.
    Labovitz, C., Ahuja, A., Bose, A., Jahanian, F.: Delayed internet routing convergence. IEEE/ACM Transactions Networking 9, 293–306 (2001)CrossRefGoogle Scholar
  10. 10.
    Steinder, M., Sethi, A.S.: A survey of fault localization techniques in computer networks. Science of Computer Programming 53, 165–194 (2004)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Qiu, L., Zhang, Y., Wang, F., Han, M.K., Mahajan, R.: A general model of wireless interference, pp. 171–182. ACM, NY (2007)Google Scholar
  12. 12.
    Kotz, D., Newport, C., Gray, R. S., Liu, J., Yuan, Y., Elliott, C.: Experimental evaluation of wireless simulation assumptions, Technical Report, Dartmouth College (2004) Google Scholar
  13. 13.
    Fessi A., Plattner, B., et al.: Resilience and Survivability for future networking: framework, mechanisms, and experimental evaluation, ResumeNet Deliverable D1.5 (2009)Google Scholar
  14. 14.
    Doerr, C., Smith, P., et al.: Resilience and Survivability for future networking: framework, mechanisms, and experimental evaluation, ResumeNet Deliverable D2.3a (2010) Google Scholar
  15. 15.
    Mayer, C.P., Gamer, T.: Integrating real world applications into OMNeT, Institute of Telematics, University of Karlsruhe, Karlsruhe, Germany (2008) Google Scholar
  16. 16.
    Lippmann, R., et al.: The 1999 DARPA Off-Line Intrusion Detection Evaluation. Computer Networks 34(4), 579–595 (2000)CrossRefGoogle Scholar
  17. 17.
    Mahoney, M.V., Chan, P.K.: An analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for network anomaly detection. In: Vigna, G., Krügel, C., Jonsson, E. (eds.) RAID 2003. LNCS, vol. 2820, pp. 220–237. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.: A Detailed Analysis of the KDD CUP 99 Data Set. IEEE, Los Alamitos (2009)CrossRefGoogle Scholar
  19. 19.
    Brugger, T.: KDD Cup 1999 dataset considered harmful, White Paper, Department of Computer Science, University of California Davis (2007) Google Scholar
  20. 20.
    Weingartner, E., vom Lehn, H., Wehrle, K.: A performance comparison of recent network simulators, pp. 1–5. IEEE, Germany (2009)Google Scholar
  21. 21.
    Kargl, F., Schoch, E.: Simulation of MANETs: A qualitative comparison between JiST/SWANS and NS-2. In: International Workshop on MobiEval (2007) Google Scholar
  22. 22.
    Young, C.P., Chang, B.R., Chen, S.Y., Wang, L.C.: A Highway Traffic Simulator with Dedicated Short Range Communications Based Cooperative Collision Prediction and Warning Mechanism. IEEE, Los Alamitos (2008)CrossRefGoogle Scholar
  23. 23.
    Schmidt-Eisenlohr, F., et al.: Cumulative Noise and 5.9GHz DSRC Extensions for ns-2.28, University of Karlsruhe, Tech. Rep. (2006) Google Scholar
  24. 24.
    Johansson B., et al.: Highway Mobility And Vehicular Ad-Hoc Networks In NS-3, CiteSeerX (2010) Google Scholar
  25. 25.
    Eichler, S.: Strategies for pseudonym changes in vehicular ad hoc networks depending on node mobility. In: Proceedings of the 2007 IEEE Intelligent Vehicles Symposium (2007) Google Scholar
  26. 26.
    Orfanus, D., Lessmann, J., Janacik, P., Lachev, L.: In Performance of wireless network simulators: a case study, pp. 59–66. ACM, New York (2008)Google Scholar
  27. 27.
    Cetinkaya, E.K., Jabbar, A., Mahmood, R., Sterbenz, J.P.G.: Modelling Network Attacks and Challenges: A Simulation-based Approach. In: EDCC, Valencia, Spain (2010) Google Scholar
  28. 28.
    Varga, A.: OMNeT++ User Manual, http://www.omnetpp.org/doc/manual/usman.html
  29. 29.
    Mell, P., Hu, V., Lipmann, R., et al.: An Overview of Issues in Testing Intrusion Detection Systems, Technical Report, National Institute of Standard and Technology (2003) Google Scholar
  30. 30.
    Gamer, T., Scharf, M.: Realistic Simulation Environments for IP-based Networks. In: ICTS (2008) Google Scholar
  31. 31.
    Wuhib, F., Stadler, R.: Decentralised Service-Level Monitoring Using Network Threshold Alerts. IEEE Communications Magazine, 44 (2006) Google Scholar
  32. 32.
    Smith, P., Fry, M., et al.: Resilience and Survivability for future networking: framework, mechanisms, and experimental evaluation, ResumeNet Deliverable D2.2b (2010) Google Scholar
  33. 33.
    Rasheed, T.: Wireless Mesh Network Simulation Framework for OMNeT++, Create-Net Technical Report (2007) Google Scholar
  34. 34.
    Maureira, J.C., Dalle, O., Dujovne, D.: Generation of Realistic 802.11 Interferences in the Omnet++ INET Framework Based on Real Traffic Measurements. In: ICST (2009)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Yue Yu
    • 1
  1. 1.School of Information TechnologiesUniversity of SydneyAustralia

Personalised recommendations