Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Optimizing the Efficiency, Vulnerability and Robustness of Road-Based Para-Transit Networks Using Genetic Algorithm Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Incentive Mechanism for Cooperative Intrusion Detection: An Evolutionary Game Approach

Authors : Yunchuan Guo, Han Zhang, Lingcui Zhang, Liang Fang, Fenghua Li

Published in: Computational Science – ICCS 2018

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In Mobile Ad-Hoc Networks, cooperative intrusion detection is efficient and scalable to massively parallel attacks. However, due to concerns of privacy leak-age and resource costs, if without enough incentives, most mobile nodes are often selfish and disinterested in helping others to detect an intrusion event, thus an ef-ficient incentive mechanism is required. In this paper, we formulate the incentive mechanism for cooperative intrusion detection as an evolutionary game and achieve an optimal solution to help nodes decide whether to participate in detec-tion or not. Our proposed mechanism can deal with the problems that cooperative nodes do not own complete knowledge about other nodes. We develop a game algorithm to maximize nodes utility. Simulations demonstrate that our strategy can efficiently incentivize potential nodes to cooperate.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Detecting Wildlife in Unmanned Aerial Systems Imagery Using Convolutional Neural Networks Trained with an Automated Feedback Loop
next chapter Hybrid Genetic Algorithm for an On-Demand First Mile Transit System Using Electric Vehicles
Footnotes
Literature
1.
Loo, J., Mauri, J.L., Ortiz, J.H.: Mobile Ad-Hoc Networks: Current Status and Future Trends. CRC Press, Boca Raton (2016)
2.
Nadeem, A., Howarth, M.P.: An intrusion detection & adaptive response mechanism for MANETs. Ad Hoc Netw. 13, 368–380 (2014)CrossRef
3.
Vasilomanolakis, E., Karuppayah, S., Muhlhauser, M., Fischer, M.: Taxonomy and survey of collaborative intrusion detection. ACM Comput. Surv. 47, 1–33 (2015)CrossRef
4.
Restuccia, F., Das, S.K., Payton, J.: Incentive mechanisms for participatory sensing: survey and research challenges. ACM Trans. Sens. Netw. 12, 1–40 (2016)
5.
Shu, X., Yao, D., Bertino, E.: Privacy-preserving detection of sensitive data exposure. IEEE Trans. Inf. Forensics Secur. 10, 1092–1103 (2015)CrossRef
6.
Niksefat, S., Sadeghiyan, B., Mohassel, P., Sadeghian, S.: ZIDS: a privacy-preserving intrusion detection system using secure two-party computation protocols. Comput. J. 57, 494–509 (2014)CrossRef
7.
Do, H.G., Ng, W.K.: Privacy-preserving approach for sharing and processing intrusion alert data. In: Proceedings of IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 7–9 (2015)
8.
Reed, J., Aviv, A.J., Wagner, D., Haeberlen, A., Pierce, B.C., Smith, J.M.: Differential privacy for collaborative security. In: Proceedings of the Third European Workshop on System Security, pp. 1–7 (2010)
9.
Jin, R., He, X., Dai, H.: On the tradeoff between privacy and utility in collaborative intrusion detection systems - a game theoretical approach. In: Proceedings of the Hot Topics in Science of Security: Symposium and Bootcamp (HotSoS), pp. 45–51 (2017)
10.
Niksefat, S., Kaghazgaran, P., Sadeghiyan, B.: Privacy issues in intrusion detection systems: a taxonomy, survey and future directions. Comput. Sci. Rev. 25, 69–78 (2017)CrossRef
11.
Hassanzadeh, A., Stoleru, R.: Towards optimal monitoring in cooperative IDS for resource constrained wireless networks. In: Proceedings of IEEE ICCCN, pp. 1–8 (2011)
12.
Gil Perez, M., Tapiador, J.E., Clark, J.A., Martnez Perez, G., Skarmeta Gomez, A.F.: Trustworthy placements: improving quality and resilience in collaborative attack detection. Comput. Netw. 58, 70–86 (2014)CrossRef
13.
Subba, B., Biswas, S., Karmakar, S.: Enhancing effectiveness of intrusion detection systems: a hybrid approach. In: Proceedings of IEEE International Conference on Advanced Networks and Telecommunications Systems, pp. 1–6 (2016)
14.
Guo, Y., Fang, L., Geng, K., Yin, L., Li, F., Chen, L.: Real-time data incentives for IoT searches. In: IEEE International Conference on Communications (2018)
15.
Peng, D., Wu, F., Chen, G.: Data quality guided incentive mechanism design for crowdsensing. IEEE Trans. Mob. Comput. 17, 307–319 (2018)CrossRef
16.
Zheng, Z., Wu, F., Tang, S.: A budget feasible incentive mechanism for weighted coverage maximization in mobile crowdsensing. IEEE Trans. Mob. Comput. 16, 2392–2407 (2017)CrossRef
17.
Ma, Q., Zhang, S., Zhu, T., Liu, K., Zhang, L., He, W., Liu, Y.: PLP: protecting location privacy against correlation analyze attack in crowdsensing. IEEE Trans. Mob. Comput. 16, 2588–2598 (2017)CrossRef
18.
Yang, D., Xue, G., Fang, X., Tang, J.: Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing. In: Proceedings of IEEE MOBILCOM, pp. 173–184 (2012)
19.
Guo, Y., Yin, L., Liu, L., Fang, B.: Utility-based cooperative decision in cooperative authentication. In: Proceedings of IEEE INFOCOM, pp. 1006–1014 (2014)
20.
Mukhopadhyay, J., Pal, A., Mukhopadhyay, S., Singh, V.K.: Quality adaptive online double auction in participatory sensing. J. Inform. Math. Sci. 9, 571–593 (2017)
21.
Yin, L., Guo, Y., Li, F., Sun, Y., Qian, J., Vasilakos, A.: A game-theoretic approach to advertisement dissemination in ephemeral networks. World Wide Web J. 21, 241–260 (2018)CrossRef
22.
Yu, R., Kang, J., Huang, X., Xie, S., Zhang, Y., Gjessing, S.: MixGroup: accumulative pseudonym exchanging for location privacy enhancement in vehicular social networks. IEEE Trans. Dependable Secur. Comput. 13, 93–105 (2016)CrossRef
23.
Cowden, C.C.: Game theory, evolutionary stable strategies and the evolution of biological interactions. Nat. Educ. Knowl. 3, paper 6 (2012)
24.
Boyd, S.P., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)CrossRef
Metadata
Title
Incentive Mechanism for Cooperative Intrusion Detection: An Evolutionary Game Approach
Authors
Yunchuan Guo
Han Zhang
Lingcui Zhang
Liang Fang
Fenghua Li
Copyright Year
2018
Book
Computational Science – ICCS 2018

Print ISBN: 978-3-319-93697-0

Electronic ISBN: 978-3-319-93698-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93698-7_7

Premium Partner

    Image Credits