Review of Potential Attacks on Robotic Swarms

  • Ian SargeantEmail author
  • Allan Tomlinson
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)


Much research has been undertaken in the area of swarm robotics and applications the vast majority of which assumes a benign operational environment. We assume a hostile environment and review the threats to robotic swarms. We then define an adversary’s capabilities and classify generic attack types and attack vectors that may be undertaken by this adversary. We conclude that while the threats to a swarm are no different to those for traditional systems, there are differences with the attack types and attack vectors. This is primarily due to the ability to manipulate the emergent behaviour of the swarm by attacking individual swarm elements. We identify characteristics of the swarm that allow such attacks to take place.


Robotic swarm Security Attacks 


  1. 1.
    Scerri, P., Pynadath, D., Johnson, L., Rosenbloom, P., Si, M., Schurr, N., Tambe, M.: A prototype infrastructure for distributed robot-agent-person teams. In: Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, ser. AAMAS 2003, pp. 433–440. ACM, New York (2003).
  2. 2.
    Subchan, S., White, B., Tsourdos, A., Shanmugavel, M., Zbikowski, R.: IMTC 2008, pp. 501–506. IEEE (2008)Google Scholar
  3. 3.
    Woern, H., Szymanski, M., Seyfried, J.: The 15th IEEE International Symposium on ROMAN 2006, September 2006, pp. 492–496 (2006)Google Scholar
  4. 4.
    Martinoli, A., Easton, K., Agassounon, W.: Modeling swarm robotic systems: a case study in collaborative distributed manipulation. Int. J. Robot. Res. 23, 415–436 (2004).
  5. 5.
    Fredslund, J., Mataric, M.J.: A general algorithm for robot formations using local sensing and minimal communication. IEEE Trans. Robot. Autom. 18(5), 837–846 (2002).
  6. 6.
    Balch, T., Arkin, R.C.: Behavior-based formation control for multi-robot teams. IEEE Trans. Robot. Autom. 14, 926–939 (1997).
  7. 7.
    Cianci, C.M., Raemy, X., Pugh, J., Martinoli, A.: Communication in a swarm of miniature robots: the e-puck as an educational tool for swarm robotics. In: Swarm Robotics: Second International Workshop, SAB 2006, Rome, Italy, September 30-October 1, 2006, Revised Selected Papers, pp. 103–115. Springer, Berlin, Heidelberg (2007).
  8. 8.
    Gerkey, B.P., Mataric, M.J.: Sold!: auction methods for multirobot coordination. IEEE Trans. Robot. Autom. 18(5), 758–768 (2002).
  9. 9.
    Higgins, F., Tomlinson, A., Martin, K.M.: Threats to the swarm: security considerations for swarm robotics. Int. J. Adv. Secur. 2(2 & 3), 288–297 (2009)Google Scholar
  10. 10.
    Theraulaz, G., Bonbeau, E.: A brief history of stigmergy. Artif. Life 5(2), 97–116 (1999). doi: 10.1162/106454699568700 CrossRefGoogle Scholar
  11. 11.
    Winfield, A.F.T., Nembrini, J.: Safety in numbers: fault tolerance in robot swarms. Int. J. Model. Ident. Control 1(1), 30–37 (2006)CrossRefGoogle Scholar
  12. 12.
    Yamashita, A., Arai, T., Ota, J., Asama, H.: Motion planning of multiple mobile robots for cooperative manipulation and transportation. IEEE Trans. Robot. Autom. 19(2), 223–237 (2003)CrossRefGoogle Scholar
  13. 13.
    Winfield, A.F.T., Harper, C.J., Nembrini, J.: Towards dependable swarms and a new discipline of swarm engineering. In: Proceedings of the 2004 International Conference on Swarm Robotics, ser. SAB 2004, pp. 126–142. Springer, Heidelberg (2005). doi: 10.1007/978-3-540-30552-1_11
  14. 14.
    Şahin, E., Girgin, S., Bayindir, L., Turgut, A.E.: Swarm robotics. In: Blum, C., Merkle, D. (eds.) Swarm Intelligence: Introduction and Applications. Natural Computing Series, pp. 87–100. Springer, Heidelberg (2008)Google Scholar
  15. 15.
    Parker, L.E.: Alliance: an architecture for fault tolerant multirobot cooperation. IEEE Trans. Robot. Autom. 14(2), 220–240 (1998)MathSciNetCrossRefGoogle Scholar
  16. 16.
    De Gennaro, M., Jadbabaie, A.: Formation control for a cooperative multi-agent system using decentralized navigation functions. In: American Control Conference, p. 6 (2006)Google Scholar
  17. 17.
    Nouyan, S., Gro, R., Bonani, M., Mondada, F., Dorigo, M.: Teamwork in self-organized robot colonies. IEEE Trans. Evol. Comput. 13(4), 695–711 (2009)CrossRefGoogle Scholar
  18. 18.
    Shen, W.-M., Will, P., Galstyan, A., Chuong, C.-M.: Hormone-inspired self-organization and distributed control of robotic swarms. Auton. Robots 17(1), 93–105 (2004)CrossRefGoogle Scholar
  19. 19.
    Gerla, M., Yi, Y., Xu, K., Hong, X.: Team communications among airborne swarms. In: Proceedings of Aerospace Conference, 2003, vol. 3, pp. 1303–1312. IEEE, March 2003Google Scholar
  20. 20.
    Fukuda, T., Funato, D., Sekiyama, K., Arai, F.: Evaluation on flexibility of swarm intelligent system. In: ICRA, pp. 3210–3215. IEEE Computer Society (1998).
  21. 21.
    Jin, K., Liang, P., Beni, G.: Stability of synchronized distributed control of discrete swarm structures. In: ICRA, pp. 1033–1038. IEEE Computer Society (1994).
  22. 22.
    Purnamadjaja, A., Russell, R.A.: Pheromone communication: implementation of necrophoric bee behaviour in a robot swarm. In: 2004 IEEE Conference on Robotics, Automation and Mechatronics, vol. 2, pp. 638–643 (2004)Google Scholar
  23. 23.
    Dai, Y.-S., Hinchey, M., Madhusoodan, M., Rash, J., Zou, X.: A prototype model for self-healing and self-reproduction in swarm robotics system. In: 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing, pp. 3–10 (2006)Google Scholar
  24. 24.
    Chapman, E., Sahin, F.: Application of swarm intelligence to the mine detection problem. In: SMC, vol. 6, pp. 5429–5434 (2004)Google Scholar
  25. 25.
    Kumar, M.V., Sahin, F.: A swarm intelligence based approach to the mine detection problem. In: 2002 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, p. 6 (2002)Google Scholar
  26. 26.
    Munirajan, V.K., Sahin, F.: Cognitive maps in swarm robots for the mine detection application. In: SMC, pp. 3364–3369 (2003)Google Scholar
  27. 27.
    Tan, Y.C., Bishop, B.: Evaluation of robot swarm control methods for underwater mine countermeasures. In: Proceedings of the Thirty-Sixth Southeastern Symposium on System Theory, 2004, pp. 294–298 (2004)Google Scholar
  28. 28.
    BS ISO/IEC 27005:2011 - Information technology - Security techniques - Information security risk management, British Standards StdGoogle Scholar
  29. 29.
    BS ISO/IEC 13335–1 Information Technology - Security Techniques - Management of Information and Communications Technology Security, British Standards StdGoogle Scholar
  30. 30.
    Ford, W., Baum, M.S.: Secure Electronic Commerce: Building The Infrastructure For Digital Signatures And Encryption. Prentice Hall PTR, Upper Saddle River (2000)Google Scholar
  31. 31.
    Whitman, M., Mattord, H.: Principles of Information Security. Cengage Learning, Boston (2011)Google Scholar
  32. 32.
    Sargeant, I., Tomlinson, A.: Modelling malicious entities in a robotic swarm. In: 2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC), pp. 7B1-1–7B1-12, October 2013Google Scholar
  33. 33.
    Swiderski, F., Snyder, W.: Threat Modelling. Microsoft Press, New York (2004)Google Scholar
  34. 34.
    Shields, C.: What do we mean by network denial of service. In: Proceedings of the 2002 IEEE Workshop on Information Assurance and Security, vol. 4 (2002)Google Scholar
  35. 35.
    Naik Bhukya, W., Suresh Kumar, G., Negi, A.: A study of effectiveness in masquerade detection. In: 2006 IEEE Region 10 Conference TENCON 2006, pp. 1–4, November 2006Google Scholar
  36. 36.
    Pejovic, V., Bojanic, S., Carreras, C., Nieto-Taladriz, O.: Detecting masquerading attack in software and in hardware. In: Electrotechnical Conference, MELECON 2006, pp. 836–838. IEEE Mediterranean, May 2006Google Scholar
  37. 37.
    Anand, M., Ives, Z., Lee, I.: Quantifying eavesdropping vulnerability in sensor networks. In: Proceedings of the 2nd International Workshop on Data management for Sensor Networks, pp. 3–9. ACM (2005)Google Scholar
  38. 38.
    Scarfone, K., Dicoi, D., Sexton, M., Tibbs, C.: Guide to securing legacy ieee 802.11 wireless networks. NIST Special Publ. 800, 48 (2008)Google Scholar
  39. 39.
    Chai, Q., Gong, G., Engels, D.: How to develop clairaudience - active eavesdropping in passive RFID systems. In: 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–6, June 2012Google Scholar
  40. 40.
    Kelly, S., Clancy, T.C.: Control and provisioning of wireless access points (CAPWAP) threat analysis for IEEE 802.11 deployments. Network Working Group, Internet Engineering Task Force (2009)Google Scholar
  41. 41.
    CESG, HMG IA Standard No. 1 - Technical Risk Assessment, CESG Std., Rev. 3.51, October 2009Google Scholar
  42. 42.
    Tretmans, J.: Testing concurrent systems: a formal approach. In: Baeten, J.C.M., Mauw, S. (eds.) CONCUR 1999. LNCS, vol. 1664, pp. 46–65. Springer, Heidelberg (1999). doi: 10.1007/3-540-48320-9_6
  43. 43.
    Gong, L.: Variations on the themes of message freshness and replay-or the difficulty in devising formal methods to analyze cryptographic protocols. In: Proceedings of Computer Security Foundations Workshop VI, 1993, pp. 131–136, June 1993Google Scholar
  44. 44.
    Roosta, T., Shieh, S., Sastry, S.: Taxonomy of security attacks in sensor networks and countermeasures. In: The First IEEE International Conference on System Integration and Reliability Improvements, Hanoi, pp. 13–15 (2006)Google Scholar
  45. 45.
    Becher, A., Benenson, Z., Dornseif, M.: Tampering with motes: real-world physical attacks on wireless sensor networks. In: Clark, J.A., Paige, R.F., Polack, F.A.C., Brooke, P.J. (eds.) SPC 2006. LNCS, vol. 3934, pp. 104–118. Springer, Heidelberg (2006). doi: 10.1007/11734666_9
  46. 46.
    Tang, S., Mark, B.: Analysis of virus spread in wireless sensor networks: An epidemic model. In: 7th International Workshop on Design of Reliable Communication Networks, DRCN 2009, pp. 86–91, October 2009Google Scholar
  47. 47.
    Tang, H., Sun, R.-L., Kong, W.-Q.: Wireless intrusion detection for defending against tcp syn flooding attack and man-in-the-middle attack. In: 2009 International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1464–1470, July 2009Google Scholar
  48. 48.
    Kumar, V., Chakraborty, S., Barbhuiya, F., Nandi, S.: Detection of stealth man-in-the-middle attack in wireless lan. In: 2012 2nd IEEE International Conference on Parallel Distributed and Grid Computing (PDGC), pp. 290–295, December 2012Google Scholar
  49. 49.
    Chen, Z., Guo, S., Zheng, K., Yang, Y.: International Conference on WiCom 2007, September 2007, pp. 2255–2258 (2007)Google Scholar
  50. 50.
    Hansen, J.A., Hansen, N.M.: A taxonomy of vulnerabilities in implantable medical devices. In: Proceedings of the Second Annual Workshop On Security And Privacy In Medical And Home-care Systems, pp. 13–20. ACM (2010)Google Scholar
  51. 51.
    Luo, X., Ji, X., Park, M.-S.: Location privacy against traffic analysis attacks in wireless sensor networks. In: 2010 International Conference on Information Science and Applications (ICISA), pp. 1–6, April 2010Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Information Security GroupRoyal Holloway, University of LondonEgham, SurreyUK

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