Skip to main content
SpringerLink
Log in

Collaborative Intrusion Detection System for Internet of Things Using Distributed Ledger Technology: A Survey on Challenges and Opportunities

  • Conference paper
  • First Online:
Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13757))

Included in the following conference series:

Abstract

This review presents the current state-of-the-art of the Distributed Ledger Technology (DLT) model used in the Collaborative Intrusion Detection System (CIDS) for anomaly detection in Internet of Things (IoT) network. The distributed IoT ecosystem has many cybersecurity problems related to anomalous activities on the network. CIDS technology is usually applied to detect anomalous activities on the IoT network. CIDS is suitable for IoT network because they have the same distributed characteristic. The use of DLT technology is expected to be able to help the IDS system accelerate detection and increase the accuracy of detection through a collaborative detection mechanism. This review will look more deeply at the placement strategies, detection method, security threat, and validation & testing method from CIDS with DLT-based for IoT network. This review also discusses the open issue and the lesson learned at the end of the review. The result is expected to produce the next research topic and help professionals design effective CIDS based on DLT for the IoT network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Commun. Surv. Tutorials 17(4), 2347–2376 (2015). https://doi.org/10.1109/COMST.2015.2444095

    Article  Google Scholar 

  2.  Wardana, A.A.,  Rakhmatsyah, A.,  Minarno, A.E.,  Anbiya, D.R.: Internet of Things Platform for Manage Multiple Message Queuing Telemetry Transport Broker Server. Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Contr. 4(3),  197–206 (2019).  https://doi.org/10.22219/kinetik.v4i3.841

  3. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., Zhao, W.: A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125–1142 (2017). https://doi.org/10.1109/JIOT.2017.2683200

    Article  Google Scholar 

  4. Zarpelão, B.B., Miani, R.S., Kawakani, C.T., de Alvarenga, S.C.: A survey of intrusion detection in Internet of Things. J. Netw. Comput. Appl. 84(February), 25–37 (2017). https://doi.org/10.1016/j.jnca.2017.02.009

    Article  Google Scholar 

  5. Zhou, C.V., Leckie, C., Karunasekera, S.: A survey of coordinated attacks and collaborative intrusion detection. Comput. Secur. 29(1), 124–140 (2010). https://doi.org/10.1016/j.cose.2009.06.008

    Article  Google Scholar 

  6. Arshad, J., Azad, M.A., Amad, R., Salah, K., Alazab, M., Iqbal, R.: A review of performance, energy and privacy of intrusion detection systems for IoT. Electron. 9(4), 1–24 (2020). https://doi.org/10.3390/electronics9040629

    Article  Google Scholar 

  7. Meng, W., Tischhauser, E.W.,  Wang, Q., Wang, Y.,  Han, J.: When intrusion detection meets blockchain technology: a review.  IEEE Access, 6(c), 10179–10188 (2018).  https://doi.org/10.1109/ACCESS.2018.2799854

  8.  Al’Aziz, B.A.A., Sukarno, P.,  Wardana, A.A.: Blacklisted IP distribution system to handle DDoS attacks on IPS Snort based on Blockchain.  Proceeding - 6th Inf. Technol. Int. Semin. ITIS 2020 41–45 (2020).  https://doi.org/10.1109/ITIS50118.2020.9320996

  9.  Benaddi, H., Ibrahimi, K. : A Review: Collaborative Intrusion Detection for IoT integrating the Blockchain technologies. In:  Proceedings of the 2020 International Conference Wireless Networks Mobile Communication. WINCOM 2020,(2020).  https://doi.org/10.1109/WINCOM50532.2020.9272464

  10. Verma, A., Ranga, V.: Security of RPL based 6LoWPAN networks in the internet of things: a review. IEEE Sens. J. 20(11), 5666–5690 (2020). https://doi.org/10.1109/JSEN.2020.2973677

    Article  Google Scholar 

  11. Rauchs, M.,  et al.: Distributed Ledger Technology Systems: A Conceptual Framework. SSRN Electron. J. (2018) https://doi.org/10.2139/ssrn.3230013

  12. Kannengießer, N., Lins, S.,  Dehling, T., Sunyaev, A.: Trade-offs between Distributed Ledger Technology Characteristics. ACM Comput. Surv. 53(2), 42:1–42:37 (2020)  https://doi.org/10.1145/3379463

  13. El Ioini, N., Pahl, C.: A review of distributed ledger technologies. In: Panetto, H., Debruyne, C., Proper, H.A., Ardagna, C.A., Roman, D., Meersman, R. (eds.) On the Move to Meaningful Internet Systems. OTM 2018 Conferences. Lecture Notes in Computer Science, vol. 11230, pp. 277–288. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02671-4_16

    Chapter  Google Scholar 

  14. Siano, P., De Marco, G., Rolan, A., Loia, V.: A survey and evaluation of the potentials of distributed ledger technology for peer-to-peer transactive energy exchanges in local energy markets. IEEE Syst. J. 13(3), 3454–3466 (2019). https://doi.org/10.1109/JSYST.2019.2903172

    Article  Google Scholar 

  15. Putra, G.D.,  Dedeoglu, V.,  Kanhere, S.S., Jurdak, R.: Poster abstract: Towards scalable and trustworthy decentralized collaborative intrusion detection system for IoT. In: Proc. - 5th ACM/IEEE Conf. Internet Things Des. Implementation, IoTDI 2020, pp. 256–257 (2020).  https://doi.org/10.1109/IoTDI49375.2020.00035

  16. Li, W., Tug, S., Meng, W., Wang, Y.: Designing collaborative blockchained signature-based intrusion detection in IoT environments. Futur. Gener. Comput. Syst. 96, 481–489 (2019). https://doi.org/10.1016/j.future.2019.02.064

    Article  Google Scholar 

  17. Mirsky, Y., Golomb, T., Elovici, Y.: Lightweight collaborative anomaly detection for the IoT using blockchain. J. Parallel Distrib. Comput. 145, 75–97 (2020). https://doi.org/10.1016/j.jpdc.2020.06.008

    Article  Google Scholar 

  18. Kumar, P., Kumar, R., Gupta, G.P., Tripathi, R.: A distributed framework for detecting DDoS attacks in smart contract-based blockchain-IoT systems by leveraging fog computing. Trans. Emerg. Telecommun. Technol. 32(6), 1–31 (2021). https://doi.org/10.1002/ett.4112

    Article  Google Scholar 

  19. Golomb, T.,  Mirsky, Y.,  Elovici, Y.: CIoTA: Collaborative Anomaly Detection via Blockchain (2018).  https://doi.org/10.14722/diss.2018.23003

  20.  Hu, B.,  Zhou,  C., Tian, Y.C., Qin, Y., Junping, X.: A Collaborative Intrusion Detection Approach Using Blockchain for Multimicrogrid Systems.  IEEE Trans. Syst. Man, Cybern. Syst.  49( 8), 1720–1730 (2019). https://doi.org/10.1109/TSMC.2019.2911548

  21. Alkadi, O., Moustafa, N., Turnbull, B., Choo, K.K.R.: A deep blockchain framework-enabled collaborative intrusion detection for protecting IoT and cloud networks. IEEE Internet Things J. 8(12), 9463–9472 (2021). https://doi.org/10.1109/JIOT.2020.2996590

    Article  Google Scholar 

  22. Vasilomanolakis, E., Karuppayah, S., Muhlhauser, M., Fischer, M.: Taxonomy and survey of collaborative intrusion detection. ACM Comput. Surv. 47(4), 1–33 (2015). https://doi.org/10.1145/2716260

    Article  Google Scholar 

  23.  Liao, H.J.,  Richard Lin,  C.H.,  Lin, Y.C.,  Tung, K.Y.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1) 16–24 (2013).  https://doi.org/10.1016/j.jnca.2012.09.004

  24. Alaba, F.A., Othman, M., Hashem, I.A.T., Alotaibi, F.: Internet of things security: a survey. J. Netw. Comput. Appl. 88(April), 10–28 (2017). https://doi.org/10.1016/j.jnca.2017.04.002

    Article  Google Scholar 

  25. Jiang, J.,  Li, Z.,  Tian, Y.,  Al-Nabhan, N.: A Review of Techniques and Methods for IoT Applications in Collaborative Cloud-Fog Environment.  Secur. Commun. Netw. 2020, (2020).  https://doi.org/10.1155/2020/8849181

  26. Da Xu, L., He, W., Li, S.: Internet of things in industries: a survey. IEEE Trans. Ind. Informatics 10(4), 2233–2243 (2014). https://doi.org/10.1109/TII.2014.2300753

    Article  Google Scholar 

  27. Warzynski, A., Kolaczek, G.: Intrusion detection systems vulnerability on adversarial examples. In: 2018 IEEE International Confernce Innovation Intelligence System Application INISTA 2018,pp.  31–34 (2018). https://doi.org/10.1109/INISTA.2018.8466271

  28.  Khraisat, A.,  Gondal, I.,  Vamplew, P.,  Kamruzzaman, J., Alazab, A.: A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks. Electron. 8(11),  (2019).  https://doi.org/10.3390/electronics8111210

  29. Asharf, J.,  Moustafa, N.,  Khurshid, H., Debie, E.,  Haider, W., Wahab, A.: A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions. Electron. 9(7), 1–4 (2020).  https://doi.org/10.3390/electronics9071177

  30. Dasgupta, D., Shrein, J.M., Gupta, K.D.: A survey of blockchain from security perspective. J. Banking Financial Technol. 3(1), 1–17 (2018). https://doi.org/10.1007/s42786-018-00002-6

    Article  Google Scholar 

  31. Antal, C., Cioara, T., Anghel, I., Antal, M., Salomie, I.: Distributed ledger technology review and decentralized applications development guidelines. Futur. Internet 13(3), 1–32 (2021). https://doi.org/10.3390/fi13030062

    Article  Google Scholar 

  32.  Natarajan, H.,  Krause, S.K.,  Gradstein, H.L.: Distributed Ledger Technology (DLT) and Blockchain. FinTech Note, 1, pp. 1–60, (2017). http://hdl.handle.net/10986/29053%0Ahttp://documents.worldbank.org/curated/en/177911513714062215/pdf/122140-WP-PUBLIC-Distributed-Ledger-Technology-and-Blockchain-Fintech-Notes.pdf

    Google Scholar 

  33. Chowdhury, M.J.M., et al.: A comparative analysis of distributed ledger technology platforms. IEEE Access 7, 167930–167943 (2019). https://doi.org/10.1109/ACCESS.2019.2953729

    Article  Google Scholar 

  34.  Farahani, B.,  Firouzi, F., Luecking, M.: The convergence of IoT and distributed ledger technologies (DLT): Opportunities, challenges, and solutions. J. Netw. Comput. Appl. 177 102936 (2021). https://doi.org/10.1016/j.jnca.2020.102936

  35. Pandl, K.D., Thiebes, S., Schmidt-Kraepelin, M., Sunyaev, A.: On the convergence of artificial intelligence and distributed ledger technology: a scoping Review and future research agenda. IEEE Access 8, 57075–57095 (2020). https://doi.org/10.1109/ACCESS.2020.2981447

    Article  Google Scholar 

  36.  Harris, J.D., Waggoner, B.: Decentralized and collaborative AI on blockchain. In: Proceedings of the 2019 2nd IEEE International Conference Blockchain,Blockchain 2019(2) 368–375 (2019). https://doi.org/10.1109/Blockchain.2019.00057

  37.  Montes, G.A.,  Goertzel, B.: Distributed, decentralized, and democratized artificial intelligence. Technol. Forecast. Soc. Change  141 2018 354–358 (2019). https://doi.org/10.1016/j.techfore.2018.11.010

  38. Li, W., Wang, Y., Li, J., Au, M.H.: Toward a blockchain-based framework for challenge-based collaborative intrusion detection. Int. J. Inf. Secur. 20(2), 127–139 (2020). https://doi.org/10.1007/s10207-020-00488-6

    Article  Google Scholar 

  39. Meng, W., Tischhauser, E.W., Wang, Q., Wang, Y., Han, J.: When intrusion detection meets blockchain technology: a review. IEEE Access 6, 10179–10188 (2018). https://doi.org/10.1109/ACCESS.2018.2799854

    Article  Google Scholar 

  40. Alkadi, O., Moustafa, N., Turnbull, B.: A review of intrusion detection and blockchain applications in the cloud: approaches, challenges and solutions. IEEE Access 8, 104893–104917 (2020). https://doi.org/10.1109/ACCESS.2020.2999715

    Article  Google Scholar 

Download references

Acknowledgements

The first author would like to thank Wroclaw University of Science and Technology (WUST) and Narodowa Agencja Wymiany Akademickiej (NAWA) for funding this research through PhD scholarship and NAWA STER scholarship. Also, thanks to Telkom University for all the support during this research and PhD studies.

Author information

Authors and Affiliations

  1. Wrocław University of Science and Technology, wybrzeże Stanisława Wyspiańskiego 27, 50-370, Wrocław, Poland

    Aulia Arif Wardana & Grzegorz Kołaczek

  2. Telkom University, Terusan Buahbatu - Bojongsoang, Jl. Telekomunikasi No. 1, Kec. Dayeuhkolot, Jawa Barat, 40257, Sukapura, Bandung, Indonesia

    Parman Sukarno

Authors
  1. Aulia Arif Wardana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Grzegorz Kołaczek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Parman Sukarno
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aulia Arif Wardana .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wardana, A.A., Kołaczek, G., Sukarno, P. (2022). Collaborative Intrusion Detection System for Internet of Things Using Distributed Ledger Technology: A Survey on Challenges and Opportunities. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21743-2_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21742-5

  • Online ISBN: 978-3-031-21743-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation