Advertisement

A Bio-Inspired Cybersecurity Schemeto Protect a Swarm of Robots

  • Alejandro Hernández-HerreraEmail author
  • Elsa Rubio Espino
  • Ponciano Jorge Escamilla Ambrosio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11289)

Abstract

Swarm robotics describes a multi-robot system characterized by the simplicity of its agents, homogeneous architecture, limited communication skills, local detection, execution of parallel tasks, robustness, scalability, flexibility and decentralized control. However, being a technology in development, the security and vulnerability of the swarm of robots against possible cybernetic attacks have been commonly overlooked. This is of major concern when executing mission-critical activities that inherently require an adequate management of security. In this work, a bio-inspired security mechanism applied to a swarm of robots is proposed. Through computer simulations, it is observed how the mechanism, when executing a homing towards a stationary landmark, allows the swarm of robots to identify abnormal behaviors, caused by a certain cyberattack; subsequently, it establishes a certain tolerance to it and allows to improve the level of availability that is required to continue executing the task at hand.

Keywords

Swarm robotics Bio-inspired cyber security scheme Cybersecurity Cyberattack 

Notes

Acknowledgments

This work was supported by IPN under Projects SIP-1894 and SIP-20180460. The first author gratefully acknowledge the support from the Mexican National Council for Science and Technology (CONACyT) and IPN-PIFI BEIFI grant to carry out this work.

References

  1. 1.
    Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)CrossRefGoogle Scholar
  2. 2.
    Brooks, R.: A robust layered control system for a mobile robot. IEEE J. Robot. Autom. 2(1), 14–23 (1986)CrossRefGoogle Scholar
  3. 3.
    Carneiro, J., et al.: When three is not a crowd: a crossregulation model of the dynamics and repertoire selection of regulatory CD4+T cells. Immunol. Rev. 216(1), 48–68 (2007)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chen, X.: Enabling disconnected transitive communication in mobile ad hoc networks. In: Proceedings of Workshop on Principles of Mobile Computing, Colocated with PODC 2001, pp. 21–27 (2001)Google Scholar
  5. 5.
    Cohen, I.: Tending Adam’s Garden: Evolving the Cognitive Immune Self. Elsevier Science (2000)Google Scholar
  6. 6.
    Dailey, K.: The FMEA Pocket Handbook: Failure Mode and Effects Analysis. DW Publishing (2004)Google Scholar
  7. 7.
    Dias, M.B., Zinck, M., Zlot, R., Stentz, A.: Robust multirobot coordination in dynamic environments. In: 2004 IEEE International Conference on Robotics and Automation, 2004, Proceedings, ICRA 2004, vol. 4, pp. 3435–3442, April 2004.  https://doi.org/10.1109/ROBOT.2004.1308785
  8. 8.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. Trans. Syst. Man Cybern. Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  9. 9.
    Fagiolini, A., Dini, G., Bicchi, A.: Distributed intrusion detection for the security of industrial cooperative robotic systems. IFAC Proc. Vol. 47(3), 7610–7615 (2014).  https://doi.org/10.3182/20140824-6-ZA-1003.02666CrossRefGoogle Scholar
  10. 10.
    Feng, R., Xu, X., Zhou, X., Wan, J.: A trust evaluation algorithm for wireless sensor networks based on node behaviors and D-S evidence theory. Sensors 11(2), 1345–1360 (2011)CrossRefGoogle Scholar
  11. 11.
    Gutiérrez, A., Campo, A., Dorigo, M., Amor, D., Magdalena, L., Monasterio-Huelin, F.: An open localization and local communication embodied sensor. Sensors 8(11), 7545–7563 (2008).  https://doi.org/10.3390/s8117545CrossRefGoogle Scholar
  12. 12.
    Hartmann, K., Steup, C.: The vulnerability of UAVs to cyber attacks - an approach to the risk assessment. In: 2013 5th International Conference on Cyber Conflict (CYCON 2013), pp. 1–23, June 2013Google Scholar
  13. 13.
    Holldobler, B., Wilson, E.O.: The Ants. The Belknap Press of Harvard University Press, Cambridge (1990)CrossRefGoogle Scholar
  14. 14.
    Holldobler, B., Wilson, E.O.: Journey to the Ants. The Belknap Press of Harvard University Press, Cambridge (1994)Google Scholar
  15. 15.
    (ISO), I.S.O.: ISO/IEC 7498–2:1989. Information processing systems - Open Systems Interconnection - Basic Reference Model - Part 2: Security Architecture. Iso7498-2:1989, pp. 1–32 (1989)Google Scholar
  16. 16.
    Janeway, C.A.J., Travers, P., Walport, M., Shlomchik, M.J.: Immunobiology: The Immune System in Health and Disease, 5th edn. Garland Science, New York (2001)Google Scholar
  17. 17.
    Javaid, A.Y., Sun, W., Devabhaktuni, V.K., Alam, M.: Cyber security threat analysis and modeling of an unmanned aerial vehicle system. In: 2012 IEEE Conference on Technologies for Homeland Security (HST), pp. 585–590, November 2012Google Scholar
  18. 18.
    Jones, E.G., Browning, B., Dias, M.B., Argall, B., Veloso, M., Stentz, A.: Dynamically formed heterogeneous robot teams performing tightly-coordinated tasks. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, pp. 570–575, May 2006Google Scholar
  19. 19.
    Kalech, M., Kaminka, G.A., Meisels, A., Elmaliach, Y.: Diagnosis of multi-robot coordination failures using distributed CSP algorithms. In: American Association for Artificial Intelligence (AAAI 2006). AAAI Press (2006)Google Scholar
  20. 20.
    Khaldi, B., Harrou, F., Cherif, F., Sun, Y.: Monitoring a robot swarm using a data-driven fault detection approach. Robot. Auton. Syst. 97, 193–203 (2017).  https://doi.org/10.1016/j.robot.2017.06.002CrossRefGoogle Scholar
  21. 21.
    Kim, A., Wampler, B., Goppert, J., Hwang, I., Aldridge, H.: Cyber attack vulnerabilities analysis for unmanned aerial vehicles. In: Infotech@Aerospace (2012)Google Scholar
  22. 22.
    Kolling, A., Walker, P., Chakraborty, N., Sycara, K., Lewis, M.: Human interaction with robot swarms: a survey. IEEE Trans. Hum.-Mach. Syst. 46(1), 9–26 (2016).  https://doi.org/10.1109/THMS.2015.2480801CrossRefGoogle Scholar
  23. 23.
    León, K., Lage, A., Carneiro, J.: Tolerance and immunity in a mathematical model of T-cell mediated suppression. J. Theor. Biol. 225(1), 107–126 (2003).  https://doi.org/10.1016/S0022-5193(03)00226-1MathSciNetCrossRefGoogle Scholar
  24. 24.
    León, K., Peréz, R., Lage, A., Carneiro, J.: Modelling T-cell-mediated suppression dependent on interactions in multicellular conjugates. J. Theor. Biol. 207(2), 231–254 (2000).  https://doi.org/10.1006/jtbi.2000.2169CrossRefGoogle Scholar
  25. 25.
    Mansfield, K., Eveleigh, T., Holzer, T.H., Sarkani, S.: Unmanned aerial vehicle smart device ground control station cyber security threat model. In: 2013 IEEE International Conference on Technologies for Homeland Security (HST), pp. 722–728, November 2013.  https://doi.org/10.1109/THS.2013.6699093
  26. 26.
    Mansfield, K., Eveleigh, T., Holzer, T.H., Sarkani, S.: DoD comprehensive military unmanned aerial vehicle smart device ground control station threat model. Publ. Def. Acquis. Univ. 23(2), 240–273 (2015)Google Scholar
  27. 27.
    Meng, Y., Nickerson, J.V., Gan, J.: Multi-robot aggregation strategies with limited communication. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2691–2696, October 2006Google Scholar
  28. 28.
    Mondada, F., et al.: The e-puck, a robot designed for education in engineering. In: Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, vol. 1, no. 1, pp. 59–65 (2009). ISBN 978-972-99143-8-6Google Scholar
  29. 29.
    Nembrini, J., University of the West of England: Minimalist coherent swarming of wireless networked autonomous mobile robots. Ph.D. thesis, University of the West of England (2005)Google Scholar
  30. 30.
    Nickerson, J.V.: A concept of communication distance and its application to six situations in mobile environments. IEEE Trans. Mob. Comput. 4(5), 409–419 (2005).  https://doi.org/10.1109/TMC.2005.60CrossRefGoogle Scholar
  31. 31.
    Nickerson, J.V., Olariu, S.: A measure for integration and its application to sensor networks. In: Workshop on Information Technologies and Systems (WITS), August 2005Google Scholar
  32. 32.
    Parker, L.E.: Alliance: an architecture for fault tolerant multirobot cooperation. IEEE Trans. Robot. Autom. 14(2), 220–240 (1998)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Pinciroli, C., et al.: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6(4), 271–295 (2012)CrossRefGoogle Scholar
  34. 34.
    Schoonderwoerd, R., Holland, O.E., Bruten, J.L., Rothkrantz, L.J.M.: Ant-based load balancing in telecommunications networks. Adapt. Behav. 5(2), 169–207 (1997)CrossRefGoogle Scholar
  35. 35.
    Strohmeier, M., Lenders, V., Martinovic, I.: On the security of the automatic dependent surveillance-broadcast protocol. IEEE Commun. Surv. Tutor. 17(2), 1066–1087 (2015)CrossRefGoogle Scholar
  36. 36.
    The Common Criteria: (22 de Junio de 2018). https://www.commoncriteriaportal.org
  37. 37.
    Timmis, J., Ismail, A., Bjerknes, J., Winfield, A.: An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems. Biosystems 146, 60–76 (2016).  https://doi.org/10.1016/j.biosystems.2016.04.001. Information Processing in Cells and TissuesCrossRefGoogle Scholar
  38. 38.
    Timmis, J., Andrews, P., Hart, E.: On artificial immune systems and swarm intelligence. Swarm Intell. 4(4), 247–273 (2010)CrossRefGoogle Scholar
  39. 39.
    Vain, J., Tammet, T., Kuusik, A., Juurik, S.: Towards scalable proofs of robot swarm dependability. In: 2008 11th International Biennial Baltic Electronics Conference, pp. 199–202, October 2008.  https://doi.org/10.1109/BEC.2008.4657513
  40. 40.
    Viksnin, I., Iureva, R., Komarov, I., Drannik, A.: Assessment of stability of algorithms based on trust and reputation model. In: Proceedings of the 18th Conference of Open Innovations Association FRUCT, FRUCT 2018, pp. 364–369. FRUCT Oy, Helsinki, Finland (2016).  https://doi.org/10.1109/FRUCT-ISPIT.2016.7561551
  41. 41.
    Zikratov, I.A., Lebedev, I.S., Gurtov, A.V.: Trust and reputation mechanisms for multi-agent robotic systems. In: Balandin, S., Andreev, S., Koucheryavy, Y. (eds.) NEW2AN 2014. LNCS, vol. 8638, pp. 106–120. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10353-2_10CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Alejandro Hernández-Herrera
    • 1
    Email author
  • Elsa Rubio Espino
    • 1
  • Ponciano Jorge Escamilla Ambrosio
    • 1
  1. 1.Centro de Investigación en Computación - Instituto Politécnico NacionalMexico CityMexico

Personalised recommendations