Black Holes and Revelations: Using Evolutionary Algorithms to Uncover Vulnerabilities in Disruption-Tolerant Networks

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• 作者：Doina Bucur (15)
  Giovanni Iacca (16)
  Giovanni Squillero (17)
  Alberto Tonda (18)
  
  15. Johann Bernoulli Institute ; University of Groningen ; Nijenborgh 9 ; 9747 AG ; Groningen ; The Netherlands
  16. INCAS3 ; Dr. Nassaulaan 9 ; 9401 HJ ; Assen ; The Netherlands
  17. Politecnico di Torino ; Corso Duca degli Abruzzi 24 ; 10129 ; Torino ; Italy
  18. INRA UMR 782 GMPA ; 1 Avenue Lucien Br茅tigni猫res ; 78850 ; Thiverval-Grignon ; France
  
• 关键词：Disruption ; tolerant network ; Routing ; Evolutionary algorithm
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9028
• 期：1
• 页码：29-41
• 全文大小：1,180 KB
• 参考文献：1. Jain, S., Fall, K., Patra, R.: Routing in a delay tolerant network. In: Proceedings of the 2004 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, SIGCOMM 2004, pp. 145鈥?58. ACM, New York (2004)
  2. Borrel, V., Ammar, M.H., Zegura, E.W.: Understanding the wireless and mobile network space: a routing-centered classification. In: Proceedings of the Second ACM Workshop on Challenged Networks, CHANTS 2007, pp. 11鈥?8. ACM, New York (2007)
  3. Jenkins, A., Kuzminsky, S., Gifford, K., Pitts, R., Nichols, K.: Delay/disruption-tolerant networking: flight test results from the international space station. In: Aerospace Conference, 2010 IEEE, pp. 1鈥?, March 2010
  4. Burgess, J., Gallagher, B., Jensen, D., Levine, B.: Maxprop: routing for vehicle-based disruption-tolerant networks. In: INFOCOM 2006, 25th IEEE International Conference on Computer Communications, pp. 1鈥?1, April 2006
  5. Li, F., Wu, J., Srinivasan, A.: Thwarting blackhole attacks in disruption-tolerant networks using encounter tickets. In: INFOCOM 2009, IEEE, pp. 2428鈥?436, April 2009
  6. Li, Q, Gao, W, Zhu, S, Cao, G (2013) To lie or to comply: defending against flood attacks in disruption tolerant networks. IEEE Trans. Dependable Secure Comput. 10: pp. 168-182 CrossRef
  7. Burgess, J., Bissias, G.D., Corner, M.D., Levine, B.N.: Surviving attacks on disruption-tolerant networks without authentication. In: Proceedings of the 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2007, pp. 61鈥?0. ACM, New York (2007)
  8. Sanchez, E, Schillaci, M, Squillero, G (2011) Evolutionary Optimization: The $$\mu $$ GP Toolkit. Springer, New York CrossRef
  9. Bucur, D, Iacca, G, Squillero, G, Tonda, A (2014) The impact of topology on energy consumption for collection tree protocols: an experimental assessment through evolutionary computation. Appl. Soft Comput. 16: pp. 210-222 CrossRef
  10. Bucur, D., Iacca, G., Squillero, G., Tonda, A.: The tradeoffs between data delivery ratio and energy costs in wireless sensor networks: a multi-objective evolutionary framework for protocol analysis. In: Proceedings of the Sixtienth Annual Conference on Genetic and Evolutionary Computation Conference, GECCO 2014. ACM, New York (2014)
  11. Ker盲nen, A., Ott, J., K盲rkk盲inen, T.: The ONE simulator for DTN protocol evaluation. In: SIMUTools 2009, Proceedings of the 2nd International Conference on Simulation Tools and Techniques, New York, NY, USA, ICST (2009)
  
• 作者单位：Applications of Evolutionary Computation
• 丛书名：978-3-319-16548-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

A challenging aspect in open ad hoc networks is their resilience against malicious agents. This is especially true in complex, urban-scale scenarios where numerous moving agents carry mobile devices that create a peer-to-peer network without authentication. A requirement for the proper functioning of such networks is that all the peers act legitimately, forwarding the needed messages, and concurring to the maintenance of the network connectivity. However, few malicious agents may easily exploit the movement patterns in the network to dramatically reduce its performance. We propose a methodology where an evolutionary algorithm evolves the parameters of different malicious agents, determining their types and mobility patterns in order to minimize the data delivery rate and maximize the latency of communication in the network. As a case study, we consider a fine-grained simulation of a large-scale disruption-tolerant network in the city of Venice. By evolving malicious agents, we uncover situations where even a single attacker can hamper the network performance, and we correlate the performance decay to the number of malicious agents.