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Detecting Forwarding Misbehavior In Clustered IoT Networks

Published:25 October 2018Publication History

ABSTRACT

Internet of Things (IoT) devices in clustered wireless networks can be compromised by compromising the gateway which they are associated with. In such scenarios, an adversary who has compromised the gateway can affect the network's performance by deliberately dropping the packets transmitted by the IoT devices. In this way, the adversary can actually mimic a bad radio channel. Hence, the affected IoT device has to retransmit the packet which will drain its battery at a faster rate. To detect such an attack, we propose a centralized detection system in this paper. It uses the uplink packet drop probability of the IoT devices to monitor the behavior of the gateway with which they are associated. The detection rule proposed is given by the generalized likelihood ratio test, where the attack probabilities are estimated using maximum likelihood estimation. Results presented show the effectiveness of the proposed detection mechanism and also demonstrate the impact of the choice of system parameters on the detection algorithm.

References

  1. 2017. IEEE Standard for Information technology--Telecommunications and information exchange between systems - Local and metropolitan area networks-- Specific requirements - Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 2: Sub 1 GHz License Exempt Operation. IEEE Std 802.11ah-2016 (May 2017), 1--594.Google ScholarGoogle Scholar
  2. Ameer Ahmed Abbasi and Mohamed Younis. 2007. A survey on clustering algorithms for wireless sensor networks. Computer communications 30, 14 (2007), 2826--2841. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Nalam Venkata Abhishek, Teng Joon Lim, Biplab Sikdar, and Anshoo Tandon. 2018. An Intrusion Detection System for Detecting Compromised Gateways in Clustered IoT Networks. In 2018 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR). IEEE, 1--6.Google ScholarGoogle Scholar
  4. Shanzhi Chen, Hui Xu, Dake Liu, Bo Hu, and Hucheng Wang. 2014. A vision of IoT: Applications, challenges, and opportunities with china perspective. IEEE Internet of Things journal 1, 4 (2014), 349--359.Google ScholarGoogle ScholarCross RefCross Ref
  5. Dave Evans. 2011. The Internet of Things: How the Next Evolution of the Internet Is Changing Everything. https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdfGoogle ScholarGoogle Scholar
  6. Fatma Gara, Leila Ben Saad, and Rahma Ben Ayed. 2017. An intrusion detection system for selective forwarding attack in IPv6-based mobile WSNs. In Wireless Communications and Mobile Computing Conference (IWCMC), 2017 13th International. IEEE, 276--281.Google ScholarGoogle ScholarCross RefCross Ref
  7. Jayavardhana Gubbi, Rajkumar Buyya, Slaven Marusic, and Marimuthu Palaniswami. 2013. Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems 29, 7 (2013), 1645--1660. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Md Mahmud Hossain, Maziar Fotouhi, and Ragib Hasan. 2015. Towards an analysis of security issues, challenges, and open problems in the internet of things. In Services (SERVICES), 2015 IEEE World Congress on. IEEE, 21--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Steven M Kay. 1993. Fundamentals of statistical signal processing, volume I: estimation theory. (1993). Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Steven M Kay. 1998. Fundamentals of statistical signal processing, Vol. II: Detection Theory. Signal Processing. Upper Saddle River, NJ: Prentice Hall (1998).Google ScholarGoogle Scholar
  11. Sunho Lim and Lauren Huie. 2015. Hop-by-Hop cooperative detection of selective forwarding attacks in energy harvesting wireless sensor networks. In Computing, Networking and Communications (ICNC), 2015 International Conference on. IEEE, 315--319.Google ScholarGoogle ScholarCross RefCross Ref
  12. Kim Thuat Nguyen, Maryline Laurent, and Nouha Oualha. 2015. Survey on secure communication protocols for the Internet of Things. Ad Hoc Networks 32 (2015), 17--31. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Cong Pu and Sunho Lim. 2016. A light-weight countermeasure to forwarding misbehavior in wireless sensor networks: design, analysis, and evaluation. IEEE Systems Journal (2016).Google ScholarGoogle Scholar
  14. Rapeepat Ratasuk, Nitin Mangalvedhe, and Amitava Ghosh. 2015. Extending LTE coverage for machine type communications. In Internet of Things (WF-IoT), 2015 IEEE 2nd World Forum on. IEEE, 193--197. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Shahid Raza, Linus Wallgren, and Thiemo Voigt. 2013. SVELTE: Real-time intrusion detection in the Internet of Things. Ad hoc networks 11, 8 (2013), 2661--2674. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Ju Ren, Yaoxue Zhang, Kuan Zhang, and Xuemin Shen. 2016. Adaptive and channel-aware detection of selective forwarding attacks in wireless sensor networks. IEEE Transactions on Wireless Communications 15, 5 (2016), 3718--3731.Google ScholarGoogle ScholarCross RefCross Ref
  17. Ahmad-Reza Sadeghi, Christian Wachsmann, and Michael Waidner. 2015. Security and privacy challenges in industrial internet of things. In Design Automation Conference (DAC), 2015 52nd ACM/EDAC/IEEE. IEEE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Chanatip Tumrongwittayapak and Ruttikorn Varakulsiripunth. 2009. Detecting sinkhole attack and selective forwarding attack in wireless sensor networks. In Information, Communications and Signal Processing, 2009. ICICS 2009. 7th International Conference on. IEEE, 1--5. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Linus Wallgren, Shahid Raza, and Thiemo Voigt. 2013. Routing Attacks and Countermeasures in the RPL-based Internet of Things. International Journal of Distributed Sensor Networks 9, 8 (2013), 794326.Google ScholarGoogle ScholarCross RefCross Ref
  20. Mahdi Zamani and Mahnush Movahedi. 2013. Machine learning techniques for intrusion detection. arXiv preprint arXiv:1312.2177 (2013).Google ScholarGoogle Scholar
  21. Bruno Bogaz Zarpelão, Rodrigo Sanches Miani, Cláudio Toshio Kawakani, and Sean Carlisto de Alvarenga. 2017. A Survey of Intrusion Detection in Internet of Things. Journal of Network and Computer Applications (2017). Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Conferences
        Q2SWinet'18: Proceedings of the 14th ACM International Symposium on QoS and Security for Wireless and Mobile Networks
        October 2018
        153 pages
        ISBN:9781450359634
        DOI:10.1145/3267129
        • General Chair:
        • Peter Müller,
        • Program Chair:
        • Ahmed Mostefaoui

        Copyright © 2018 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 25 October 2018

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        Overall Acceptance Rate46of131submissions,35%

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