Skip to main content

A Blockchain-Based Machine Learning Intrusion Detection System for Internet of Things

  • Chapter
  • First Online:
Principles and Practice of Blockchains
  • 781 Accesses

Abstract

The Internet of Things (IoT) is the major evolution of Internet also known as Internet of Everything which made a network with smart sensors heterogeneous devices. Nowadays, the usability of IoT networks is increasing very rapidly from smart home, smart industry to smart everything. But, these smart devices like as traditional Internet are vulnerable to various attacks such as denial of service (DoS), spoofing attacks, ransomware attacks, and many more. There are also various protocols such as DTLS, IPv6, and many other lightweight protocols used for IoT data security. But despite these, these attacks are also occurred via sniffing or manipulating of header information to both encrypted and non-encrypted protocols. Attacks generated via header information can be mitigated by various methods as ML-based intrusion detection systems (IDSs) is one of them. These IDSs security depends on the accuracy/integrity of training data (IoT data) and trust on the ML/DL algorithms. Recently, blockchain, a new advanced technology, is emerged, which has several use cases in the IoT domain for providing security. Due to the various advantages of blockchain and ML/DL methods in IoT data security, we combine these technologies and provide a secure blockchain-ML-based framework for heterogeneous IoT data security environment.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. M.A. Khan, K. Salah, IoT security: review, blockchain solutions, and open challenges. Futur. Gener. Comput. Syst. 82, 395–411 (2018)

    Article  Google Scholar 

  2. J. Kaur, A Secure and Smart Framework for Preventing Ransomware Attack. arXiv preprint arXiv:2001.07179 (2020)

    Google Scholar 

  3. M.A. Al-Garadi, et al., A survey of machine and deep learning methods for Internet of Things (IoT) security. arXiv preprint arXiv:1807.11023 (2018)

    Google Scholar 

  4. F. Restuccia, S. D’Oro, T. Melodia, Securing the Internet of Things in the age of machine learning and software-defined networking. IEEE Internet Things J. 5(6), 4829–4842 (2018)

    Article  Google Scholar 

  5. S. Raza, L. Wallgren, T. Voigt, SVELTE: Real-time intrusion detection in the Internet of Things. Ad Hoc Netw. 11(8), 2661–2674 (2013)

    Article  Google Scholar 

  6. R. Doshi, N. Apthorpe, N. Feamster, Machine learning DDoS detection for consumer Internet of Things devices, in IEEE Security and Privacy Workshops (SPW) (IEEE, New York, 2018)

    Google Scholar 

  7. J. Kaur, MAC layer management frame denial of service attacks, in International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE) (IEEE, New York, 2016)

    Google Scholar 

  8. J. Kaur, Wired LAN and Wireless LAN attack detection using signature based and machine learning tools, in Networking Communication and Data Knowledge Engineering (Springer, Singapore, 2018), pp. 15–24

    Google Scholar 

  9. A. Dvir, L. Buttyan, VeRA-version number and rank authentication in RPL, in IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems (IEEE, New York, 2011)

    Google Scholar 

  10. H. Perrey, et al., TRAIL: Topology authentication in RPL. arXiv preprint arXiv:1312.0984 (2013)

    Google Scholar 

  11. D. Airehrour, J.A. Gutierrez, S.K. Ray, SecTrust-RPL: A secure trust-aware RPL routing protocol for Internet of Things. Futur. Gener. Comput. Syst. 93, 860–876 (2019)

    Article  Google Scholar 

  12. J. Kaur, A ultimate approach of mitigating attacks in RPL based low power lossy networks, arXiv preprint arXiv:1910.13435 (2019)

    Google Scholar 

  13. F.I. Khan, et al., Wormhole attack prevention mechanism for RPL based LLN network, in Proceedings of the Fifth International Conference on Ubiquitous and Future Networks (ICUFN) (IEEE, New York, 2013)

    Google Scholar 

  14. K. Weekly, K. Pister, Evaluating sinkhole defense techniques in RPL networks, in Proceedings of the 20th IEEE International Conference on Network Protocols (ICNP) (IEEE, New York, 2012)

    Google Scholar 

  15. F. Ahmed, Y.-B. Ko, Mitigation of black hole attacks in Routing Protocol for Low Power and Lossy Networks. Secur. Commun. Netw. 9(18), 5143–5154 (2016)

    Article  Google Scholar 

  16. J. Granjal, E. Monteiro, J.S. Silva, Enabling network-layer security on IPv6 wireless sensor networks, in IEEE Global Telecommunications Conference GLOBECOM (IEEE, New York, 2010)

    Google Scholar 

  17. P.N. Mahalle, et al., Identity authentication and capability based access control (IACAC) for the Internet of Things. Journal of Cyber Security and Mobility 1(4), 309–348 (2013)

    Google Scholar 

  18. S. Raza, et al., Securing Internet of Things with lightweight IPsec (2010)

    Google Scholar 

  19. S. Raza, T. Voigt, V. Jutvik, Lightweight IKEv2: a key management solution for both the compressed IPsec and the IEEE 802.15. 4 security, in Proceedings of the IETF Workshop on Smart Object Security, vol. 23 (2012)

    Google Scholar 

  20. Top IoT Vulnerabilities, OWASP, Top IoT Vulnerabilities (2016). https://www.owasp.org/index.php/Top_IoT_Vulnerabilities [Retrieved: Sep,2018]

  21. D. Conzon, et al., The Virtus middleware: An XMPP based architecture for secure IoT communications, in Proceedings of the 21st International Conference on Computer Communications and Networks (ICCCN) (IEEE, New York, 2012)

    Google Scholar 

  22. J. Granjal, E. Monteiro, J. Silva, Application-layer security for the WoT: extending CoAP to support end-to-end message security for Internet-integrated sensing applications, International Conference on Wired/Wireless Internet Communication (Springer, Berlin, 2013)

    Google Scholar 

  23. M. Sethi, Arkko, J., Keränen, A., End-to-end security for sleepy smart object networks, in Proceedings of the 37th Annual IEEE Conference on Local Computer Networks-Workshops (IEEE, New York, 2012)

    Google Scholar 

  24. M. Brachmann, et al., End-to-end transport security in the IP-based Internet of Things, in Proceedings of the 21st International Conference on Computer Communications and Networks (ICCCN) (IEEE, New York, 2012)

    Google Scholar 

  25. A. Reyna, et al., On blockchain and its integration with IoT: Challenges and opportunities. Futur. Gener. Comput. Syst. 88, 173–190 (2018)

    Article  Google Scholar 

  26. M. Banerjee, J. Lee, K.-K.R. Choo, A blockchain future for Internet of Things security: A position paper. Digital Commun. Networks 4(3), 149–160 (2018)

    Article  Google Scholar 

  27. S. Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System (2008)

    Google Scholar 

  28. J. Kaur, 10 Blockchain simulators and testnets for all your testing needs, in Hackernoon (2020). https://hackernoon.com/blockchain-simulators-ui2030z0 [Retrived: 28 Jan, 2020]

  29. G. Wood, Ethereum: A secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper 151, 1–32 (2014)

    Google Scholar 

  30. V. Singla, et al., Develop leave application using blockchain smart contract, in Proceedings of the 11th International Conference on Communication Systems & Networks (COMSNETS) (IEEE, New York, 2019)

    Google Scholar 

  31. J. Kaur, V. Singla, S. Kalra, A Blockchain Based Solution for Securing Data of IoT Devices, in International Conference on Service-Oriented Computing (Springer, Cham, 2019)

    Google Scholar 

  32. R. Ameer, What Is Hyperledger? The Most Comprehensive Guide Ever!’ (2017). https://blockgeeks.com/guides/hyperledger/, [Retrieved: Feb,2019]

  33. G. Greenspan, Multichain private blockchain-white paper (2015). http://www.multichain. com/download/MultiChain-White-Paper.pdf.

  34. M. Samaniego, R. Deters, Blockchain as a Service for IoT, in IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (IEEE, New York, 2016)

    Google Scholar 

  35. Popov, Serguei, The tangle. cit (2016), p. 131

    Google Scholar 

  36. J. Pan, et al., EdgeChain: An edge-IoT framework and prototype based on blockchain and smart contracts. IEEE Internet Things J. 6(3), 4719–4732 (2018)

    Article  Google Scholar 

  37. A. Dorri, S.S. Kanhere, R. Jurdak, Blockchain in Internet of Things: challenges and solutions. arXiv preprint arXiv:1608.05187 (2016)

    Google Scholar 

  38. F. Gilles, W. Bendella, E. Alves, Blockchain-Based Decentralized Cloud Computing, in iExec Corporation (2018)

    Google Scholar 

  39. Y. Sun, et al., Blockchain-enabled wireless Internet of Things: performance analysis and optimal communication node deployment. IEEE Internet Things J. 6(3), 5791–5802 (2019)

    Article  Google Scholar 

  40. K. Jaspreet, A semi supervised hybrid protection for network and host based attacks. J. Eng. Appl. Sci. 12(12), 3108–3112 (2017)

    Google Scholar 

  41. J. Linus, O. Olsson, Improving Intrusion Detection for IoT Networks- A Snort GPGPU Modification Using OpenCL, Master’s Thesis (Department of CSE, Chalmers University of Technology and University of Gothenburg, Gothenburg, 2018)

    Google Scholar 

  42. A. Sforzin, et al., RPiDS: Raspberry Pi IDS—A Fruitful Intrusion Detection System for IoT, in International IEEE Conferences on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld) (IEEE, New York, 2016)

    Google Scholar 

  43. M. Bikash, Do we need only AI or IoT or ML or BlockChain or all of them together? 2(019). http://www.bikashmohanty.com/topics/do-we-need-only-ai-or-iot-or-ml-or-blockchain-or-all-of-them-together.html, [Retrieved: March,2019]

  44. Yann300, Remix Documentation-Release 1 (2018). https://buildmedia.readthedocs.org/media/pdf/remix/latest/remix.pdf [Retrieved:Nov,2018]

  45. Low Orbit Ion Cannon, Wikipedia: The Free Encyclopedia (2018). https://en.wikipedia.org/wiki/Low_Orbit_Ion_Cannon [Retrieved: Oct,2018]

  46. C. Francois, Keras Documentation (2015). https://keras.io [Retrieved:Dec,2018]

  47. Long short-term memory, Wikipedia: The Free Encyclopedia (2018). https://en.wikipedia.org/wiki/Long_short-term_memory [Retrieved: Sep,2018]

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jaspreet Kaur .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kaur, J., Singh, G. (2023). A Blockchain-Based Machine Learning Intrusion Detection System for Internet of Things. In: Daimi, K., Dionysiou, I., El Madhoun, N. (eds) Principles and Practice of Blockchains. Springer, Cham. https://doi.org/10.1007/978-3-031-10507-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10507-4_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10506-7

  • Online ISBN: 978-3-031-10507-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics