Skip to main content
SpringerLink
Log in

AI-Empowered Blockchain Techniques Against Cybersecurity Context in IoT: A Survey

  • Chapter
  • First Online:
Artificial Intelligence for Sustainable Development

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

  • 65 Accesses

Abstract

The Internet of Things (IoT) is a vast network made up of connected-internet items that use software’s installed to exchange data. Numerous Internet of Things (IoT) solutions have been created over the past 20 years by small, medium-sized, and major businesses to improve our quality of life. The need for more robust cybersecurity safeguards is becoming more critical as technology develops. Despite the fact that they are both different in nature and have the capacity to provide a variety of threat detection techniques, artificial intelligence and blockchain can work together or even stand alone to significantly improve cybersecurity. The latest attack vectors must be thwarted in this era of digitization, making cybersecurity crucial. Small enterprises, major corporations, and even individuals are all targets of cyberattacks. Cybercriminals are always developing new exploits to take advantage of vulnerabilities as the threat landscape evolves. Artificial Intelligence can be used to analyze enormous volumes of information or data to spot patterns and abnormalities that can be used to detect and thwart cyberattacks. Additionally, it can automate repetitive processes, freeing up human specialists to concentrate on trickier security problems. In this article, it examines how blockchain technology and artificial intelligence (AI) are transforming the internet of things (IoT) from cybersecurity. Some challenges and unresolved issues are mentioned in order to guide future research and stimulate more investigation of this subject that is becoming more and more relevant. This study elaborates on important future prospects that could be investigated by scholars to push this discipline even further.

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 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
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

References

  1. T. T. A. Dinh, R. Liu, M. Zhang, G. Chen, B. C. Ooi, and J. Wang, “Untangling Blockchain: A Data Processing View of Blockchain Systems,” IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 7, pp. 1366–1385, Jul. 2018, https://doi.org/10.1109/tkde.2017.2781227.

  2. J. Zou, B. Ye, L. Qu, Y. Wang, M. A. Orgun, and L. Li, “A Proof-of-Trust Consensus Protocol for Enhancing Accountability in Crowdsourcing Services,” IEEE Transactions on Services Computing, vol. 12, no. 3, pp. 429–445, May 2019, https://doi.org/10.1109/tsc.2018.2823705.

  3. A. Reyes-Yanes, S. Gelio, P. Martinez, and R. Ahmad, “Wireless Sensing Module for IoT Aquaponics Database Construction,” International Journal of Electronics and Electrical Engineering, vol. 9, no. 2, pp. 43–47, Jun. 2021, https://doi.org/10.18178/ijeee.9.2.43-47.

  4. S. Alharbi, A. Attiah, and D. Alghazzawi, “Integrating Blockchain with Artificial Intelligence to Secure IoT Networks: Future Trends,” Sustainability, vol. 14, no. 23, p. 16002, Nov. 2022, https://doi.org/10.3390/su142316002.

  5. S. Aldhaheri, D. Alghazzawi, L. Cheng, A. Barnawi, and B. A. Alzahrani, “Artificial Immune Systems approaches to secure the internet of things: A systematic review of the literature and recommendations for future research,” Journal of Network and Computer Applications, vol. 157, p. 102537, May 2020, https://doi.org/10.1016/j.jnca.2020.102537.

  6. M. Shen, X. Tang, L. Zhu, X. Du, and M. Guizani, “Privacy-Preserving Support Vector Machine Training Over Blockchain-Based Encrypted IoT Data in Smart Cities,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 7702–7712, Oct. 2019, https://doi.org/10.1109/jiot.2019.2901840.

  7. Y. Liu, F. R. Yu, X. Li, H. Ji, and V. C. M. Leung, “Blockchain and Machine Learning for Communications and Networking Systems,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1392–1431, 2020, https://doi.org/10.1109/comst.2020.2975911.

  8. S. K. Singh, S. Rathore, and J. H. Park, “BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence,” Future Generation Computer Systems, vol. 110, pp. 721–743, Sep. 2020, https://doi.org/10.1016/j.future.2019.09.002.

  9. X. Han, R. Zhang, X. Liu, and F. Jiang, “Biologically Inspired Smart Contract: A Blockchain-Based DDoS Detection System,” 2020 IEEE International Conference on Networking, Sensing and Control (ICNSC), Oct. 2020, https://doi.org/10.1109/icnsc48988.2020.9238104.

  10. M. Kowalski, Z. W. Y. Lee, and T. K. H. Chan, “Blockchain technology and trust relationships in trade finance,” Technological Forecasting and Social Change, vol. 166, p. 120641, May 2021, https://doi.org/10.1016/j.techfore.2021.120641.

  11. P. Sandner, J. Gross, and R. Richter, “Convergence of Blockchain, IoT, and AI,” Frontiers in Blockchain, vol. 3, Sep. 2020, https://doi.org/10.3389/fbloc.2020.522600.

  12. R. Vishwakarma and A. K. Jain, “A survey of DDoS attacking techniques and defence mechanisms in the IoT network,” Telecommunication Systems, vol. 73, no. 1, pp. 3–25, Jul. 2019, https://doi.org/10.1007/s11235-019-00599-z.

  13. S. Singh, P. K. Sharma, B. Yoon, M. Shojafar, G. H. Cho, and I.-H. Ra, “Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city,” Sustainable Cities and Society, vol. 63, p. 102364, Dec. 2020, https://doi.org/10.1016/j.scs.2020.102364.

  14. B. D. Deebak and F. AL-Turjman, “Privacy-preserving in smart contracts using blockchain and artificial intelligence for cyber risk measurements,” Journal of Information Security and Applications, vol. 58, p. 102749, May 2021, https://doi.org/10.1016/j.jisa.2021.102749.

  15. K. R. Ozyilmaz and A. Yurdakul, “Designing a Blockchain-Based IoT With Ethereum, Swarm, and LoRa: The Software Solution to Create High Availability With Minimal Security Risks,” IEEE Consumer Electronics Magazine, vol. 8, no. 2, pp. 28–34, Mar. 2019, https://doi.org/10.1109/mce.2018.2880806.

  16. T. A. Ahanger, “Defense Scheme to Protect IoT from Cyber Attacks using AI Principles,” International Journal of Computers Communications & Control, vol. 13, no. 6, pp. 915–926, Nov. 2018, https://doi.org/10.15837/ijccc.2018.6.3356.

  17. H. F. Atlam, R. J. Walters, and G. B. Wills, “Intelligence of Things: Opportunities & Challenges,” 2018 3rd Cloudification of the Internet of Things (CIoT), Jul. 2018, https://doi.org/10.1109/ciot.2018.8627114.

  18. Y. Qian et al., “Towards decentralized IoT security enhancement: A blockchain approach,” Computers & Electrical Engineering, vol. 72, pp. 266–273, Nov. 2018, https://doi.org/10.1016/j.compeleceng.2018.08.021.

  19. J. Nieminen et al., “Networking solutions for connecting bluetooth low energy enabled machines to the internet of things,” IEEE Network, vol. 28, no. 6, pp. 83–90, Nov. 2014, https://doi.org/10.1109/mnet.2014.6963809.

  20. K. E. Jeon, J. She, P. Soonsawad, and P. C. Ng, “BLE Beacons for Internet of Things Applications: Survey, Challenges, and Opportunities,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 811–828, Apr. 2018, https://doi.org/10.1109/jiot.2017.2788449.

  21. C. Wohlin, E. Mendes, K. R. Felizardo, and M. Kalinowski, “Guidelines for the search strategy to update systematic literature reviews in software engineering,” Information and Software Technology, vol. 127, p. 106366, Nov. 2020, https://doi.org/10.1016/j.infsof.2020.106366.

  22. B. Wu, Q. Li, K. Xu, R. Li, and Z. Liu, “SmartRetro: Blockchain-Based Incentives for Distributed IoT Retrospective Detection,” 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Oct. 2018, https://doi.org/10.1109/mass.2018.00053.

  23. S. R and A. H, “Improved EPOA clustering protocol for lifetime longevity in wireless sensor network,” Sensors International, vol. 3, p. 100199, 2022, https://doi.org/10.1016/j.sintl.2022.100199.

  24. Evans Mwiti Ochieng, “A Study of the History, Functions, Roles, and Challenges of Human Resources Management”, Journal of Enterprise and Business Intelligence, vol.3, no.1, pp. 054–064, January 2023. https://doi.org/10.53759/5181/JEBI202303006.

  25. A. Haldorai, J. Sivaraj, M. Nagabushanam, and M. Kingston Roberts, “Cognitive Wireless Networks Based Spectrum Sensing Strategies: A Comparative Analysis,” Applied Computational Intelligence and Soft Computing, vol. 2022, pp. 1–14, Oct. 2022, https://doi.org/10.1155/2022/6988847.

    Article  Google Scholar 

  26. W. Li, S. Tug, W. Meng, and Y. Wang, “Designing collaborative blockchained signature-based intrusion detection in IoT environments,” Future Generation Computer Systems, vol. 96, pp. 481–489, Jul. 2019, https://doi.org/10.1016/j.future.2019.02.064.

  27. G. Spathoulas, N. Giachoudis, G.-P. Damiris, and G. Theodoridis, “Collaborative Blockchain-Based Detection of Distributed Denial of Service Attacks Based on Internet of Things Botnets,” Future Internet, vol. 11, no. 11, p. 226, Oct. 2019, https://doi.org/10.3390/fi11110226.

  28. I. Dutt, S. Borah, and I. K. Maitra, “Immune System Based Intrusion Detection System (IS-IDS): A Proposed Model,” IEEE Access, vol. 8, pp. 34929–34941, 2020, https://doi.org/10.1109/access.2020.2973608.

    Article  Google Scholar 

  29. M. A. Cheema, H. Khaliq Qureshi, C. Chrysostomou, and M. Lestas, “Utilizing Blockchain for Distributed Machine Learning based Intrusion Detection in Internet of Things,” 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS), May 2020, https://doi.org/10.1109/dcoss49796.2020.00074.

  30. S. Aldhaheri, D. Alghazzawi, L. Cheng, B. Alzahrani, and A. Al-Barakati, “DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System,” Applied Sciences, vol. 10, no. 6, p. 1909, Mar. 2020, https://doi.org/10.3390/app10061909.

  31. P. Nespoli, F. G. Marmol, and J. M. Vidal, “A Bio-Inspired Reaction Against Cyberattacks: AIS-Powered Optimal Countermeasures Selection,” IEEE Access, vol. 9, pp. 60971–60996, 2021, https://doi.org/10.1109/access.2021.3074021.

    Article  Google Scholar 

  32. J. Ashraf et al., “IoTBoT-IDS: A novel statistical learning-enabled botnet detection framework for protecting networks of smart cities,” Sustainable Cities and Society, vol. 72, p. 103041, Sep. 2021, https://doi.org/10.1016/j.scs.2021.103041.

  33. A. A. Ghali, R. Ahmad, and H. Alhussian, “A Framework for Mitigating DDoS and DOS Attacks in IoT Environment Using Hybrid Approach,” Electronics, vol. 10, no. 11, p. 1282, May 2021, https://doi.org/10.3390/electronics10111282.

  34. P. Kumar, R. Kumar, G. P. Gupta, and R. Tripathi, “A Distributed framework for detecting DDoS attacks in smart contract-based Blockchain-IoT Systems by leveraging Fog computing,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 6, Sep. 2020, https://doi.org/10.1002/ett.4112.

  35. Dua, D.; Graff, C. Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences. 2017. Available online: http://archive.ics.uci.edu/ml (accessed on 16 January 2022).

  36. X. Qu et al., “A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection,” Mobile Networks and Applications, vol. 26, no. 2, pp. 808–829, Oct. 2019, https://doi.org/10.1007/s11036-019-01353-0.

  37. S. Walling and S. Lodh, “Performance Evaluation of Supervised Machine Learning Based Intrusion Detection with Univariate Feature Selection on NSL KDD Dataset,” Feb. 2023, https://doi.org/10.21203/rs.3.rs-2537820/v1.

Download references

Author information

Authors and Affiliations

  1. Centre for Future Networks and Digital Twin, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Anandakumar Haldorai

  2. Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Babitha Lincy R, Suriya Murugan & Minu Balakrishnan

Authors
  1. Anandakumar Haldorai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Babitha Lincy R
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suriya Murugan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Minu Balakrishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2024 European Alliance for Innovation

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Haldorai, A., R, B.L., Murugan, S., Balakrishnan, M. (2024). AI-Empowered Blockchain Techniques Against Cybersecurity Context in IoT: A Survey. In: Artificial Intelligence for Sustainable Development. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-53972-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-53972-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53971-8

  • Online ISBN: 978-3-031-53972-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation