A method to detect intrusion into the Internet of Things using the game theory

Document Type : Original Article

Authors

1 Assistant Professor, Computer Department, Tehran Payam Noor University, Tehran, Iran

2 Master's degree, Tehran Payam Noor University, Tehran, Iran

3 Master's degree, Department of Information Technology Management, Tehran Payam Noor University, Tehran, Iran

Abstract

The Internet of Things is an emerging technology that integrates the Internet and physical intelligent objects; objects that cover a wide range of areas such as smart homes and cities, industrial and military processes, human health, business and agriculture. The IoT technology deepens the presence of Internet-connected devices in our day-to-day operations, bringing many benefits to the quality of life, as well as security-related challenges. Accordingly, the IoT security solutions should be developed and like other networks, the IoT intrusion detection systems are considered the most important means of providing security. In the present study, a method is proposed to detect any IoT intrusion, using the game theory. In the proposed method, the attacker security attack game and the behavior of the intrusion detection system in a two-player, non-participatory game are analyzed dynamically and with complete information, and the equilibrium solutions are obtained for specific sub-games. The analysis of best response parameters using the game theory and Nash equilibrium definitions indicates the need to use cloud-fog-based IoT intrusion detection systems. This is realized in the proposed model through providing optimal strategies and maximizing the attack reports by the smart node network.

Keywords

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References

[1]     A. Marosi, E. Zabah, H. Ataee khabaz.,2020. Network intrusion detection using a combination of artificial neural networks in a hierarchical manner (in Persian), Journal of Electronical & Cyber Defence ,Vol 8, pp. 89-99.
[2]     Abduvaliyev, A., Pathan, A.S.K., Jianying, Z., Roman, R., Wai-Choong, W., 2013. On the vital areas of intrusion detection systems in wireless sensor networks. IEEE 15 (3), 1223–1237.
[3]     Arwa Aldweesh, Abdelouahid Derhab., 2020. Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowledge-Based Systems. Vol 189.
[4]     Atzori, L. Iera, A., Morabiti, G., 2010. The internet of things: A survey, computer Network, V54, 15, 2787-2805.
[5]     Borgia, E., 2014. The Internet of Things vision: key features, applications and open issues. Comput Commun.54, 1-31.
[6]     Butun, I., Morgera, S., Sankar, R., 2014. A survey of intrusion detection systems in wireless sensor networks. Commun. Surv. Tutor. IEEE 16 (1), 266–282.
[7]     Butun, I., Morgera, S.D., Sankar, R., 2014. A survey of intrusion detection systems in wireless sensor networks. IEEE Commun. Surv. Tutor. 16 (1), 266–282.
[8]     Cervantes, C., Poplade, D., Nogueira, M., Santos, A., 2015. Detection of sinkhole attacks for supporting secure routing on 6LoWPAN for Internet of Things. In: 2015 IFIP/ IEEE International Symposium on Integrated Network Management (IM), 606–611.
[9]     Dewan, F., Nouri, H., & Mohammad, R., 2010, Combining Native Bayes and Decision tree for Adaptive Intrusion Detection. International Journal of Network Its Security & Application, 2, 12-25.
[10] D. Midi, A. Rullo, A. Mudgerikar, E. Bertino, Kalis., 2017. A system for knowledge-driven adaptable intrusion detection for the internet of things, in: Proceed- ings of the IEEE Thirty-Seventh International Conference on Distributed Computing System(ICDCS), 2017, 656-666.
[11] Fotini, Pavlidov, Georgios Koltsidas., 2010. Game theory for routing modeling in communication networks – a survey, Journal of Communications and Network, 10(30), 268-286.
[12] Jin, Du., 2012. Application of IOT in electronic commerce, Journal of Digital Content Technology and its Application,6.
[13] Granjal, E.Monteiro, J.s. Silva., 2015. Security for the internet of things: a survey of existing protocols and open research issues. iEEE.17(3), 1294-1312.
[14] Granjal, J., Monteiro, E., Silva, J.S., 2012. On the effectiveness of end-to-end security for Internet-integrated sensing applications. In: Green Computing and Communications (GreenCom), IEEE, 87–93.
[15] Kim, A.N., Hekland, F., Petersen, S., Doyle, P., 2008. When HART goes wireless: understanding and implementing the WirelessHART standard, In: 2008 IEEE International Conference on Emerging Technologies and Factory Automation, 899–907.
[16] ] Kim, D., Ro, W.W., 2014. A malicious pattern detection engine for embedded security systems in the Internet of Things. Sensors 14 (12), 24188–24211.
[17] Klempous, Ryszard, et al., 2007. Adaptive misbehavior detection in wireless sensors network based on local community agreement. 14th Annual IEEE International Conference and Workshops on the Engineering of Computer- Based System.
[18] Kumar, S., Dutta, K., 2016. Intrusion detection in mobile ad hoc networks: techniques, systems, and future challenges. Secur. Commun. Netw. 9 (14), 2484–2556.
[19] [19] M. Foroghi, A. Akrami zadeh, M. Bagheri.,2018. The decision model in a cyber-conflict with injury, with a game theory approach (in Persian), Journal of Electronical & Cyber Defence ,Vol 8, pp. 89-99.
[20] Mitchell, R., Chen, I.-R., 2014. A survey of intrusion detection techniques for cyberphysical systems. ACM Comput. Surv. (CSUR) 46 (4), 55.
[21] Miorandi, D., Sicari, S., De Pellegrini, F., Chlamtac, I., 2012. Internet of things: vision, applications and research challenges. Ad Hoc Netw. 10 (7), 1497–1516.
[22] Mishra, A., Nadkarni, K., Patcha, A., 2004. Intrusion detection in wireless ad hoc networks. IEEE Wirel. Commun. 11 (1), 48–60.
[23] Midi, S., Krishna, P., Agarwal, H., Saxena, A., Obaidat, M., 2011. A learning automata based solution for preventing Distributed Denial of Service in Internet of Things. In: Internet of Things (iThings/CPSCom), International Conference on and Proceedings of the 4th International Conference on Cyber, Physical and Social Computing, 114–122.
[24] Miorandi, D., Sicari, S., De Pellegrini, F., Chlamtac, I., 2012. Internet of Things: vision, applications and research challenges. Ad Hoc Netw.10(7).1497-1516.
[25] Modi, C., Patel, D., Borisaniya, B., Patel, H., Patel, A., Rajarajan, M., 2013. A survey of intrusion detection techniques in Cloud. J. Netw. Comput. Appl. 36 (1), 42–57.
[26] Mudgerikar, A., Sharma, p., & Bertino, E.,2019. A system- level Intrusion Detection System for IoT Devices. In Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security, 493-500.
[27] N. Dosti.,2019. New mechanism to enhance IoT network security using quantum and classical cryptography (in Persian), Journal of Electronical & Cyber Defence ,Vol 4.
[28] Pongle, P., Chavan, G., 2015. Real time intrusion and wormhole attack detection in Internet of Things. Int. J. Comput. Appl. 121 (9), 1–9.
[29] Le, A., Loo, J., Chai, K.K., Aiash, M., 2016. A specification-based IDS for detecting attacks on RPL-based network topology. Information 7 (2), 25. 
[30] Ramesh, T., and S. Shaleni Priya., 2018. A Review on Game Theory Based Congestion Control in Wireless Sensor Network. Journal of Network Communications and Emerging Technologies.
[31] S. Reza, L. Wallgren, T. Voigt., 2013.
 real-time intrusion detection in the internet of things, Ad Hoc Netw.11,2661-2674.
[32] Shigen Shen, Longjun Huang, Haiping Zhou, Shui Yu,. 2018. Multistage Signaling Game-based 
[33] Optimal Detection Strategies for Suppressing Malware Diffusion in Fog-Cloud-based IoT Networks, IEEE Internet of Things Journal.
[34] Sicari, S., Rizzardi, A., Grieco, L., 
Coen-Porisini, A., 2015. Security, privacy 
and trust in Internet of Things: the road ahead. Comput. Netw. 76 (0), 146–164.
[35] S. Sicari and A. Rizzardi and L. A. Grieco and A.Coenporisini., 2014, Security, privacy and trust in Iot: The road ahead, Computer Network.
Vacca, J., 2013. Computer and Information Security Handbook. Morgan Kaufmann, Amsterdam.
Electronic and Cyber Defense
Volume 10, Issue 1 - Serial Number 37
Serial No. 37, Spring Quarterly
May 2022
Pages 21-31

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History

  • Receive Date: 23 April 2021
  • Revise Date: 26 June 2021
  • Accept Date: 19 January 2022
  • Publish Date: 22 May 2022

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