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Journal of Discrete Mathematical Sciences and Cryptography cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

Implementation of IRIS authentication on industrial IoT devices for secured boot process

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* Corresponding author · click or hover a name for details

pp. 397–410Vol. 29Issue 2-AFebruary 2026DOI: 10.47974/JDMSC-2470 Crossmark XML
Received:
08 Apr 2025
Published Online:
31 Dec 2025
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2470
Pages:
397–410

Abstract

Instant Retrieval Information System (IRIS) is a hardware subsystem designed to provide IoT devices with secure boot functionality. The author presents an iris can be used to IoT devices, together with a hardware secure boot solution. IRIS has the ability to boot a Linux kernel image that has been pre-stored on removable media. It also has a data validator that ensures the boot process’s secrecy, integrity, and validity. It has been observed that with the use of field programmable gate array chips, IRIS has short boot up times and a tiny hardware footprint. Subsequently, Electronic Linux Unified Key Setup (Embedded LUKS) an open-source crypto-core, can be implemented outside of the boot loading process to add secrecy, integrity, and validity to data saved on off-chip storage, such as a flash device. Thus, IRIS demonstrated an open-source generic solution that can be tailored to many architectures. 

Keywords

Subject Classifications

68M25

References

[1] G. Cano-Quiveu, P. Ruiz-de-Clavijo-Vazquez, M. J. Bellido, J. Juan-Chico, and J. Viejo-Cortes, “IRIS: An embedded secure boot for IoT devices,” Internet of Things, vol. 23, pp. 100874 (Jul. 2023), doi: https://doi.org/10.1016/j.iot.2023.100874.
[2] S. Umer, A. Sardar, Ranjeet Kumar Rout, M. Tanveer, and I. Razzak, “IoT-Enabled Multimodal Biometric Recognition System in Secure Environment,” IEEE internet of things journal (Online), vol. 10, no. 24, pp. 21457–21466 (Dec. 2023), doi: https://doi.org/10.1109/jiot.2023.3299465.
[3] P. Vaishnav, M. Kaushik, and L. Raja, “Behavioral biometric authentication on smartphone using keystroke dynamics,” Journal of Discrete Mathematical Sciences & Cryptography/Journal of discrete mathematical sciences & cryptography, vol. 26, no. 2, pp. 591–600 (Jan. 2023), doi: https://doi.org/10.47974/jdmsc-1645.
[4] J. Zhang, Y. Zhou, R. Xi, S. Li, J. Guo, and Y. He, “Iris,” Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, vol. 7, no. 3, pp. 1–27 (Sep. 2023), doi: https://doi.org/10.1145/3610913.
[5] P. Vaishnav, L. Raja, P. Singh, and Ramakrishnan Vairavasamy, “Multilayered authentication for ATM transaction using keystroke dynamics and touch dynamics,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 4, pp. 1357–1365 (Jan. 2024), doi: https://doi.org/10.47974/jdmsc-1990.
[6] M. B. Mohammad, Prudhvi Kanth Bezawada, Harish Tanneeru, and Jitendra Janjanam, “Development of standalone iris recognition system using deep neural networks,” AIP conference proceedings, vol. 2931, pp. 090006–090006 (Jan. 2023), doi: https://doi.org/10.1063/5.0178605.
[7] S. Phani Praveen, Sai Srinivas Vellela, and B. Ramachandran, “SmartIris ML: Harnessing Machine Learning for Enhanced Multi-Biometric Authentication,” ResearchGate, vol. 4, no. 1, pp. 25–36 (Jan. 2024), Accessed: May 14, 2025. [Online]. Available: https://www.researchgate.net/publication/378439449_SmartIris_ML_Harnessing_Machine_Learning_for_Enhanced_Multi-Biometric_Authentication
[8] Muhammad Imran Zulfiqar and I. Younis, “Enhanced Security Paradigms: Converging IoT and Biometrics for Advanced Locker Protection,” IEEE Internet of Things Journal, pp. 1–1 (Jan. 2024), doi: https://doi.org/10.1109/jiot.2024.3432282.
[9] Omar Ibrahim Obaid and Saba Abdul-Baqi Salman, “Security and Privacy in IoT-based Healthcare Systems: A Review,” Mesopotamian Journal of Computer Science, pp. 29–40 (Dec. 2022), doi: https://doi.org/10.58496/mjcsc/2022/007.
[10] P. Vaishnav, L. Raja, P. Singh, and S. Tandel, “Applications of artificial intelligence in cyber security,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 4, pp. 1367–1375 (Jan. 2024), doi: https://doi.org/10.47974/jdmsc-1991.
[11] K. Saminathan, T. Chakravarthy, and M. Chithra, “Iris Recognition Based On Kernels Of Support Vector Machine,” Online) Ictact Journal On Soft Computing: Special Issue On Soft -Computing Theory, Application And Implications In Engineering And Technology, pp. 2 (2015), doi: https://doi.org/10.21917/ijsc.2015.0125.
[12] K. Nguyen, H. Proença, and F. Alonso-Fernandez, “Deep Learning for Iris Recognition: A Survey,” ACM computing surveys, vol. 56, no. 9, pp. 1–35 (Apr. 2024), doi: https://doi.org/10.1145/3651306.
[13] S. Sharma, L. Raja, V. Bhatnagar, D. Sharma, Swami Nisha Bhagirath, and Ramesh Chandra Poonia, “Hybrid HOG-SVM encrypted face detection and recognition model,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 25, no. 1, pp. 205–218 (Jan. 2022), doi: https://doi.org/10.1080/09720529.2021.2014141.
[14] P. Patel, “How Secure Boot help to Secure IoT Device,” eInfochips, Nov. 24 (2023). https://www.einfochips.com/blog/how-secure-boot-help-to-secure-IoT-device (accessed May 14, 2025).
[15] K. Bhateja, S. Sharma, S. Chaudhury, and N. Agrawal, “Iris recognition based on sparse representation and k-nearest subspace with genetic algorithm,” Pattern Recognition Letters, vol. 73, pp. 13–18 (Dec. 2015), doi: https://doi.org/10.1016/j.patrec.2015.12.009.
[16] S. Umer, B. C. Dhara, and B. Chanda, “A novel cancelable iris recognition system based on feature learning techniques,” Information Sciences, vol. 406–407, pp. 102–118 (Sep. 2017), doi: https://doi.org/10.1016/j.ins.2017.04.026.
[17] A. A. El-Latif, B. Abd-El-Atty, M. S. Hossain, S. Elmougy, and A. Ghoneim, “Secure Quantum Steganography Protocol for Fog Cloud Internet of Things,” IEEE Access, vol. 6, pp. 10332–10340 (2018), doi: https://doi.org/10.1109/access.2018.2799879.
[18] P. Dash, F. Pandey, M. Sarma, and D. Samanta, “Efficient private key generation from iris data for privacy and security applications,” Journal of Information Security and Applications, vol. 75, pp. 103506 (May 2023), doi: https://doi.org/10.1016/j.jisa.2023.103506.
[19] Kumar, P. Dadheech, V. Singh, L. Raja, and R. C. Poonia, “An enhanced quantum key distribution protocol for security authentication,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 22, no. 4, pp. 499–507 (May 2019), doi: https://doi.org/10.1080/09720529.2019.1637154.
[20] K. Hajari, U. Gawande, and Y. Golhar, “Neural Network Approach to Iris Recognition in Noisy Environment,” Procedia Computer Science, vol. 78, pp. 675–682 (2016), doi: https://doi.org/10.1016/j.procs.2016.02.116.

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