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Reseach Article

IoT Device Identity Management and Blockchain for Security and Data Integrity

by Pranav Gangwani, Santosh Joshi, Himanshu Upadhyay, Leonel Lagos
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 42
Year of Publication: 2023
Authors: Pranav Gangwani, Santosh Joshi, Himanshu Upadhyay, Leonel Lagos
10.5120/ijca2023922529

Pranav Gangwani, Santosh Joshi, Himanshu Upadhyay, Leonel Lagos . IoT Device Identity Management and Blockchain for Security and Data Integrity. International Journal of Computer Applications. 184, 42 ( Jan 2023), 49-55. DOI=10.5120/ijca2023922529

@article{ 10.5120/ijca2023922529,
author = { Pranav Gangwani, Santosh Joshi, Himanshu Upadhyay, Leonel Lagos },
title = { IoT Device Identity Management and Blockchain for Security and Data Integrity },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2023 },
volume = { 184 },
number = { 42 },
month = { Jan },
year = { 2023 },
issn = { 0975-8887 },
pages = { 49-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number42/32593-2023922529/ },
doi = { 10.5120/ijca2023922529 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:23:50.187885+05:30
%A Pranav Gangwani
%A Santosh Joshi
%A Himanshu Upadhyay
%A Leonel Lagos
%T IoT Device Identity Management and Blockchain for Security and Data Integrity
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 42
%P 49-55
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Human-human or human-device communication has traditionally been the most prevalent kind of communication, however, the Internet of Things (IoT) promises to dramatically expand the Internet by enabling machine-machine (M2M) communication. The ever-increasing reliance on data to form the bases associated with decision-making processes requires data that can be trusted emanating from known devices. These devices often contain important and confidential data such as personal credentials, financial status, health data, and other private and sensitive data. Therefore, the integrity of these devices and associated data are imperative for further usage and processing. Moreover, due to the deployment and participation of a massive number of devices in the IoT ecosystem, management of identities and mitigating security vulnerabilities are two major challenges that must be addressed. The large majority of these devices are susceptible to breaches and malicious actions compromising the integrity of their data, therefore identity validation of these devices is crucial as it is a means to ensure whether data attained from these devices can be trusted. An innovative technology called blockchain has recently been developed to address several IoT security concerns and ensure the integrity of the data collected from these IoT devices. This paper proposes a technique for IoT identity management called PUF-based Device Identity Management (PUF-DIM) that employs Physical Unclonable Function (PUF) to perform device identity management to establish trust in the data associated with each device and a device's unique identifier. Moreover,a review of the major security problems with IoT and how blockchain plays a significant role in tackling those issues is discussed. Finally, a blockchain-based IoT data integrity technique is proposed for ensuring that IoT data is authentic and tamper-proof. The presented technique incorporates the consensus mechanism as well as the chain structure within the data integrity scheme for IoT.

References
  1. R. Khan, S. U. Khan, R. Zaheer, and S. Khan, “Future internet: The internet of things architecture, possible applications and key challenges,” Proc. - 10th Int. Conf. Front. Inf. Technol. FIT 2012, pp. 257–260, 2012, doi: 10.1109/FIT.2012.53.
  2. G. S. Thejas et al., “A Multi-time-scale Time Series Analysis for Click Fraud Forecasting using Binary Labeled Imbalanced Dataset,” in 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), Dec. 2019, pp. 1–8, doi: 10.1109/CSITSS47250.2019.9031036.
  3. N. V. Dharwadkar, A. A. Dixit, A. K. Kannur, M. A. B. Kadampur, and S. Joshi, “Identification of Reasons Behind Infant Crying Using Acoustic Signal Processing and Deep Neural Network for Neonatal Intensive Care Unit,” Int. J. Inf. Retr. Res., vol. 12, no. 1, pp. 1–17, Jan. 2022, doi: 10.4018/IJIRR.289576.
  4. S. K. Peddoju, H. Upadhyay, J. Soni, and N. Prabakar, “Natural language processing based anomalous system call sequences detection with virtual memory introspection,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 5, pp. 455–460, 2020, doi: 10.14569/IJACSA.2020.0110559.
  5. X. Zhu and Y. Badr, “Identity Management Systems for the Internet of Things: A Survey Towards Blockchain Solutions,” Sensors (Basel)., vol. 18, no. 12, pp. 1–18, 2018, doi: 10.3390/s18124215.
  6. S. Joshi, H. Upadhyay, and L. Lagos, “Deactivation and decommissioning web log analysis using big data technology - 15710,” 2015, [Online]. Available: https://www.osti.gov/biblio/22824525.
  7. A. Tambe et al., “Detection of Threats to IoT Devices using Scalable VPN-forwarded Honeypots,” in Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy, Mar. 2019, pp. 85–96, doi: 10.1145/3292006.3300024.
  8. S. Tufail, S. Batool, and A. I. Sarwat, “A Comparative Study Of Binary Class Logistic Regression and Shallow Neural Network For DDoS Attack Prediction,” in SoutheastCon 2022, Mar. 2022, pp. 310–315, doi: 10.1109/SoutheastCon48659.2022.9764108.
  9. S. Tufail, I. Parvez, S. Batool, and A. Sarwat, “A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid,” Energies, vol. 14, no. 18, p. 5894, Sep. 2021, doi: 10.3390/en14185894.
  10. H. Upadhyay, L. Lagos, S. Joshi, and A. Abrahao, “Big Data Framework with Machine Learning for D and D Applications - 19108,” 2019, [Online]. Available: https://www.osti.gov/biblio/23002927.
  11. P. Gangwani, J. Soni, H. Upadhyay, and S. Joshi, “A Deep Learning Approach for Modeling of Geothermal Energy Prediction,” Int. J. Comput. Sci. Inf. Secur., vol. 18, no. 1, pp. 62–65, 2020.
  12. D. Gangwani, Q. Liang, S. Wang, and X. Zhu, “An Empirical Study of Deep Learning Frameworks for Melanoma Cancer Detection using Transfer Learning and Data Augmentation,” in 2021 IEEE International Conference on Big Knowledge (ICBK), Dec. 2021, pp. 38–45, doi: 10.1109/ICKG52313.2021.00015.
  13. D. Gangwani and P. Gangwani, “Applications of Machine Learning and Artificial Intelligence in Intelligent Transportation System: A Review,” in Choudhary, A., Agrawal, A.P., Logeswaran, R., Unhelkar, B. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, 2021, pp. 203–216.
  14. J. Soni, N. Prabakar, and H. Upadhyay, “Towards Detecting Fake Spammers Groups in Social Media: An Unsupervised Deep Learning Approach,” in Deep Learning for Social Media Data Analytics, T.-P. Hong, L. Serrano-Estrada, A. Saxena, and A. Biswas, Eds. Cham: Springer International Publishing, 2022, pp. 237–253.
  15. N. V. Dharwadkar, G. G. Shingan, S. U. Mane, and S. Joshi, “Enhanced Parallel-Particle Swarm Optimization (EP-PSO) Approach for Solving Nurse Rostering Problem,” Int. J. Swarm Intell. Res., vol. 13, no. 1, pp. 1–17, Jan. 2022, doi: 10.4018/IJSIR.298261.
  16. S. Horrow and A. Sardana, “Identity management framework for cloud based internet of things,” ACM Int. Conf. Proceeding Ser., pp. 200–203, 2012, doi: 10.1145/2490428.2490456.
  17. F. Farid, M. Elkhodr, F. Sabrina, F. Ahamed, and E. Gide, “A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services,” Sensors, vol. 21, no. 2, 2021, doi: 10.3390/s21020552.
  18. C. H. Lee and K. H. Kim, “Implementation of IoT system using block chain with authentication and data protection,” Int. Conf. Inf. Netw., vol. 2018-Janua, pp. 936–940, 2018, doi: 10.1109/ICOIN.2018.8343261.
  19. K. Yang, Q. Li, and L. Sun, “Towards automatic fingerprinting of IoT devices in the cyberspace,” Comput. Networks, vol. 148, pp. 318–327, Jan. 2019, doi: 10.1016/j.comnet.2018.11.013.
  20. N. Yousefnezhad, A. Malhi, and K. Främling, “Automated IoT Device Identification Based on Full Packet Information Using Real-Time Network Traffic,” Sensors, vol. 21, no. 8, p. 2660, Apr. 2021, doi: 10.3390/s21082660.
  21. A. Kanuparthi, R. Karri, and S. Addepalli, “Hardware and embedded security in the context of internet of things,” Proc. ACM Conf. Comput. Commun. Secur., pp. 61–65, 2013, doi: 10.1145/2517968.2517976.
  22. Y. Atwady and M. Hammoudeh, “A survey on authentication techniques for the internet of things,” ACM Int. Conf. Proceeding Ser., vol. Part F1305, pp. 15–20, 2017, doi: 10.1145/3102304.3102312.
  23. G. E. Suh and S. Devadas, “Physical unclonable functions for device authentication and secret key generation,” Proc. - Des. Autom. Conf., pp. 9–14, 2007, doi: 10.1109/DAC.2007.375043.
  24. P. Mall, R. Amin, A. K. Das, M. T. Leung, and K.-K. R. Choo, “PUF-Based Authentication and Key Agreement Protocols for IoT, WSNs, and Smart Grids: A Comprehensive Survey,” IEEE Internet Things J., vol. 9, no. 11, pp. 8205–8228, Jun. 2022, doi: 10.1109/JIOT.2022.3142084.
  25. S. Mathur, W. Trappe, N. Mandayam, C. Ye, and A. Reznik, “Radio-telepathy: Extracting a secret key from an unauthenticated wireless channel,” Proc. Annu. Int. Conf. Mob. Comput. Networking, MOBICOM, pp. 128–139, 2008, doi: 10.1145/1409944.1409960.
  26. R. Pappu, B. Recht, J. Taylor, and N. Gershenfeld, “Physical one-way functions,” Science (80-. )., vol. 297, no. 5589, pp. 2026–2030, 2002, doi: 10.1126/science.1074376.
  27. M. Ebrahimabadi, M. Younis, and N. Karimi, “A PUF-Based Modeling-Attack Resilient Authentication Protocol for IoT Devices,” IEEE Internet Things J., vol. 9, no. 5, pp. 3684–3703, Mar. 2022, doi: 10.1109/JIOT.2021.3098496.
  28. S. Joshi, H. Upadhyay, L. Lagos, N. S. Akkipeddi, and V. Guerra, “Machine Learning Approach for Malware Detection Using Random Forest Classifier on Process List Data Structure,” in Proceedings of the 2nd International Conference on Information System and Data Mining - ICISDM ’18, 2018, pp. 98–102, doi: 10.1145/3206098.3206113.
  29. C. Huth, J. Zibuschka, P. Duplys, and T. Güneysu, “Securing systems on the Internet of Things via physical properties of devices and communications,” 9th Annu. IEEE Int. Syst. Conf. SysCon 2015 - Proc., pp. 8–13, 2015, doi: 10.1109/SYSCON.2015.7116721.
  30. P. Gangwani, A. Perez-Pons, T. Bhardwaj, H. Upadhyay, S. Joshi, and L. Lagos, “Securing Environmental IoT Data Using Masked Authentication Messaging Protocol in a DAG-Based Blockchain: IOTA Tangle,” Futur. Internet, vol. 13, no. 12, p. 312, Dec. 2021, doi: 10.3390/fi13120312.
  31. A. M. Antonopoulos, Mastering Bitcoin: unlocking digital cryptocurrencies. O’Reilly Media, Inc., 2014.
  32. P. S. Kumar and S. Pranavi, “Performance analysis of machine learning algorithms on diabetes dataset using big data analytics,” 2017 Int. Conf. Infocom Technol. Unmanned Syst. Trends Futur. Dir. ICTUS 2017, vol. 2018-Janua, no. Iddm, pp. 508–513, 2018, doi: 10.1109/ICTUS.2017.8286062.
  33. S. Aruna, M. Maheswari, and A. Saranya, “Highly Secured Blockchain Based Electronic Voting System Using SHA3 and Merkle Root,” IOP Conf. Ser. Mater. Sci. Eng., vol. 993, no. 1, p. 012103, Dec. 2020, doi: 10.1088/1757-899X/993/1/012103.
  34. S. Namasudra and P. Sharma, “Achieving a Decentralized and Secure Cab Sharing System Using Blockchain Technology,” IEEE Trans. Intell. Transp. Syst., pp. 1–10, 2022, doi: 10.1109/TITS.2022.3186361.
  35. A. Sultan, M. A. Mushtaq, and M. Abubakar, “IOT Security Issues Via Blockchain,” in Proceedings of the 2019 International Conference on Blockchain Technology, Mar. 2019, pp. 60–65, doi: 10.1145/3320154.3320163.
  36. S. Namasudra, P. Sharma, R. G. Crespo, and V. Shanmuganathan, “Blockchain-Based Medical Certificate Generation and Verification for IoT-based Healthcare Systems,” IEEE Consum. Electron. Mag., pp. 1–1, 2022, doi: 10.1109/MCE.2021.3140048.
  37. A. R. Reddy and P. S. Kumar, “Predictive big data analytics in healthcare,” Proc. - 2016 2nd Int. Conf. Comput. Intell. Commun. Technol. CICT 2016, pp. 623–626, 2016, doi: 10.1109/CICT.2016.129.
  38. P. Otte, M. de Vos, and J. Pouwelse, “TrustChain: A Sybil-resistant scalable blockchain,” Futur. Gener. Comput. Syst., vol. 107, pp. 770–780, 2020, doi: 10.1016/j.future.2017.08.048.
  39. P. Sharma, N. R. Moparthi, S. Namasudra, V. Shanmuganathan, and C. Hsu, “Blockchain‐based IoT architecture to secure healthcare system using identity‐based encryption,” Expert Syst., Dec. 2021, doi: 10.1111/exsy.12915.
  40. A. O. Bang, U. P. Rao, A. Visconti, A. Brighente, and M. Conti, “An IoT Inventory Before Deployment: A Survey on IoT Protocols, Communication Technologies, Vulnerabilities, Attacks, and Future Research Directions,” Comput. Secur., vol. 123, p. 102914, Dec. 2022, doi: 10.1016/j.cose.2022.102914.
  41. M. A. Khan and K. Salah, “IoT security: Review, blockchain solutions, and open challenges,” Futur. Gener. Comput. Syst., vol. 82, pp. 395–411, 2018, doi: 10.1016/j.future.2017.11.022.
  42. C. Decker, C. Decker, and R. Wattenhofer, “Information propagation in the Bitcoin network Information Propagation in the Bitcoin Network,” 13-th IEEE Int. Conf. Peer-to-Peer Comput., no. August, pp. 1–10, 2016, doi: 10.1109/P2P.2013.6688704.
  43. Y. J. Chen, L. C. Wang, and S. Wang, “Stochastic Blockchain for IoT Data Integrity,” IEEE Trans. Netw. Sci. Eng., vol. 7, no. 1, pp. 373–384, 2020, doi: 10.1109/TNSE.2018.2887236.
Index Terms

Computer Science
Information Sciences

Keywords

Blockchain IoT Identity Management PUF