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Bandwidth Scanning when the Rivals Are Subjective

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Book cover Game Theory for Networks (GameNets 2019)

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Abstract

In this paper we consider how subjectivity affects the problem of scanning spectrum bands, and the impact on both the scanner and invader’s strategy. To model such subjective behavior, we formulate a prospect theoretical (PT) extension of the Bayesian bandwidth scanning game where the Scanner knows only a priori probabilities about what type of intrusion (e.g. regular intensity or low intensity) occurs in the spectrum bands. Existence and uniqueness of the PT Bayesian equilibrium is proven. Moreover, these PT Bayesian equilibrium strategies are derived in closed form as functions of the detection probabilities associated with different invader types. Waterfilling equations are derived, which allows one to determine these detection probabilities. Bands where the Invader’s strategies have band-sharing form are identified. The sensitivity of the strategies to the subjective factors and a priori probabilities are numerically illustrated.

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References

  1. Altman, E., Avrachenkov, K., Garnaev, A.: Generalized \(\alpha \)-fair resource allocation in wireless networks. In: 47th IEEE Conference on Decision and Control (CDC 2008), Cancun, Mexico, pp. 2414–2419 (2009)

    Google Scholar 

  2. Anindya, I.C., Kantarcioglu, M.: Adversarial anomaly detection using centroid-based clustering. In: IEEE International Conference on Information Reuse and Integration (IRI), pp. 1–8 (2018)

    Google Scholar 

  3. Baston, V.J., Garnaev, A.Y.: A search game with a protector. Naval Res. Logistics 47, 85–96 (2000)

    Article  MathSciNet  Google Scholar 

  4. Comaniciu, C., Mandayam, N.B., Poor, H.V.: AWireless Networks Multiuser Detection in Cross-Layer Design. Springer, New York (2005)

    Google Scholar 

  5. Dambreville, F., Le Cadre, J.P.: Detection of a markovian target with optimization of the search efforts under generalized linear constraints. Naval Res. Logistics 49, 117–142 (2002)

    Article  MathSciNet  Google Scholar 

  6. Digham, F.F., Alouini, M.S., Simon, M.K.: On the energy detection of unknown signals over fading channels. IEEE Trans. Commun. 55, 21–24 (2007)

    Article  Google Scholar 

  7. Garnaev, A.: A remark on a helicopter and submarine game. Naval Res. Logistics 40, 745–753 (1993)

    Article  MathSciNet  Google Scholar 

  8. Garnaev, A., Garnaeva, G., Goutal, P.: On the infiltration game. Int. J. Game Theory 26, 215–221 (1997)

    Article  MathSciNet  Google Scholar 

  9. Garnaev, A., Trappe, W.: One-time spectrum coexistence in dynamic spectrum access when the secondary user may be malicious. IEEE Trans. Inf. Forensics Secur. 10, 1064–1075 (2015)

    Article  Google Scholar 

  10. Garnaev, A., Trappe, W.: A bandwidth monitoring strategy under uncertainty of the adversary’s activity. IEEE Trans. Inf. Forensics Secur. 11, 837–849 (2016)

    Article  Google Scholar 

  11. Garnaev, A., Trappe, W., Kung, C.-T.: Optimizing scanning strategies: selecting scanning bandwidth in adversarial RF environments. In: 8th International Conference on Cognitive Radio Oriented Wireless Networks (Crowncom), pp. 148–153 (2013)

    Google Scholar 

  12. Guan, S., Wang, J., Jiang, C., Tong, J., Ren, Y.: Intrusion detection for wireless sensor networks: a multi-criteria game approach. In: IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6 (2018)

    Google Scholar 

  13. Han, Z., Niyato, D., Saad, W., Basar, T., Hjrungnes, A.: Game Theory in Wireless and Communication Networks: Theory, Models, and Applications. Cambridge University Press, New York (2012)

    MATH  Google Scholar 

  14. Hohzaki, R.: An inspection game with multiple inspectees. Eur. J. Oper. Res. 178, 894–906 (2007)

    Article  MathSciNet  Google Scholar 

  15. Hohzaki, R., Iida, K.: A search game with reward criterion. J. Oper. Res. Soc. Japan 41, 629–642 (1998)

    MathSciNet  MATH  Google Scholar 

  16. Jotshi, A., Batta, R.: Search for an immobile entity on a network. Eur. J. Oper. Res. 191, 347–359 (2008)

    Article  MathSciNet  Google Scholar 

  17. Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica 47, 263–291 (1979)

    Article  Google Scholar 

  18. Kahneman, D., Tversky, A.: Advances in prospect theory: cumulative representation of uncertainty. J. Risk Uncertainty 5, 297–323 (1992)

    Article  Google Scholar 

  19. Liu, S., Chen, Y., Trappe, W., Greenstein, L.J.: ALDO: an anomaly detection framework for dynamic spectrum access networks. In: IEEE International Conference on Computer (INFOCOM), pp. 675–683 (2009)

    Google Scholar 

  20. Poongothai, T., Jayarajan, K.: A noncooperative game approach for intrusion detection in mobile adhoc networks. In: International Conference on Computing, Communication and Networking, pp. 1–4 (2008)

    Google Scholar 

  21. Poor, H.V.: An Introduction to Signal Detection and Estimation. Springer, New York (1994). https://doi.org/10.1007/978-1-4757-2341-0

    Book  MATH  Google Scholar 

  22. Prelec, D.: The probability weighting function. Econometrica 90, 497–528 (1998)

    Article  MathSciNet  Google Scholar 

  23. Reddy, Y.B.: A game theory approach to detect malicious nodes in wireless sensor networks. In: Third International Conference on Sensor Technologies and Applications, pp. 462–468 (2009)

    Google Scholar 

  24. Saad, W., Sanjab, A., Wang, Y., Kamhoua, C.A., Kwiat, K.A.: Hardware trojan detection game: a prospect-theoretic approach. IEEE Trans. Veh. Technol. 66, 7697–7710 (2017)

    Article  Google Scholar 

  25. Sakaguchi, M.: Two-sided search games. J. Oper. Res. Soc. Japan 16, 207–225 (1973)

    MathSciNet  MATH  Google Scholar 

  26. Sanjab, A., Saad, W., Basar, T.: Prospect theory for enhanced cyber-physical security of drone delivery systems: a network interdiction game. In: IEEE International Conference on Communications (ICC), Paris, France (2017)

    Google Scholar 

  27. Sauder, D.W., Geraniotis, E.: Signal detection games with power constraints. IEEE Trans. Inf. Theory 40, 795–807 (1994)

    Article  MathSciNet  Google Scholar 

  28. Shen, S.: A game-theoretic approach for optimizing intrusion detection strategy in WSNs. In: 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC) (2011)

    Google Scholar 

  29. Vamvoudakis, K.G., Hespanha, J.P., Sinopoli, B., Mo, Y.: Adversarial detection as a zero-sum game. In: IEEE 51st IEEE Conference on Decision and Control (CDC), pp. 7133–7138 (2012)

    Google Scholar 

  30. Wang, X., Feng, R., Wu, Y., Che, S., Ren, Y.: A game theoretic malicious nodes detection model in MANETs. In: IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS), pp. 1–6 (2012)

    Google Scholar 

  31. Xiao, L., Liu, J., Li, Q., Mandayam, N.B., Poor, H.V.: User-centric view of jamming games in cognitive radio networks. IEEE Trans. Inf. Forensics Secur. 10, 2578–2590 (2015)

    Article  Google Scholar 

  32. Xiao, L., Liu, J., Li, Y., Mandayam, N.B., Poor, H.V.: Prospect theoretic analysis of anti-jamming communications in cognitive radio networks. In: IEEE Global Communications Conference, pp. 746–751 (2014)

    Google Scholar 

  33. Xu, D., Xiao, L., Mandayam, N.B., Poor, H.V.: Cumulative prospect theoretic study of a cloud storage defense game against advanced persistent threats. In: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 541–546 (2017)

    Google Scholar 

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Authors and Affiliations

  1. WINLAB, Rutgers University, North Brunswick, USA

    Andrey Garnaev & Wade Trappe

Authors
  1. Andrey Garnaev
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  2. Wade Trappe
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Corresponding author

Correspondence to Andrey Garnaev .

Editor information

Editors and Affiliations

  1. Inria Sophia Antipolis, Sophia Antipolis Cedex, France

    Konstantin Avrachenkov

  2. Tsinghua University, Beijing, China

    Longbo Huang

  3. University of California, Santa Barbara, CA, USA

    Jason R. Marden

  4. Telecom ParisTech (ENST), Paris Cedex, France

    Marceau Coupechoux

  5. Sorbonne Université, Paris Cedex, France

    Anastasios Giovanidis

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Garnaev, A., Trappe, W. (2019). Bandwidth Scanning when the Rivals Are Subjective. In: Avrachenkov, K., Huang, L., Marden, J., Coupechoux, M., Giovanidis, A. (eds) Game Theory for Networks. GameNets 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 277. Springer, Cham. https://doi.org/10.1007/978-3-030-16989-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-16989-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16988-6

  • Online ISBN: 978-3-030-16989-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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