A Novel Multi-Level Hashing Algorithm to Enhance Internet of Things Devices’ and Networks’ Security
Keywords:
Hashing algorithm, Internet of Things (IoT), power consumption, security, secure hash algorithm (SHA) familyAbstract
The Internet of Things (IoT) has emerged as a significant technological advancement in recent years. Its development has garnered global attention from many organisations, industries and researchers. The IoT is responsible for collecting and processing data from remote areas, significantly enhancing productivity for distributed systems and individuals. Secure hash algorithms (SHAs) are critical to ensuring enhanced security levels within IoT ecosystems. These algorithms generate fixed-size hash values from input data, making them useful for various security applications. This paper proposes a new multi-level hashing algorithm (MLHA) that could overcome challenges and significantly impact IoT security. The proposed MLHA was carefully considered and managed to effectively enhance IoT security without compromising the performance and efficiency of IoT devices. The new hashing algorithm is suitable for all IoT devices, ranging from small devices with limited processing power fuelled by batteries to larger devices with constant electrical power and vast resources. This study’s main objective is to develop a new hashing algorithm specifically designed for IoT devices. This research strives to enhance IoT devices’ data protection and security measures using improved hashing algorithms. This objective will be achieved by examining various IoT devices, analysing current algorithms and developing strategies that effectively balance efficiency and security. Furthermore, the suggested algorithm presents a novel methodology for enhancing data security in IoT devices. The algorithms examined in this paper are from the SHA family, which are based on bitwise operations. However, these algorithms were modified to enhance their efficiency and scalability for IoT devices. The proposed algorithm comprises eight levels, each accommodating a particular IoT device. The first level is characterised by its basic nature and is specifically tailored for devices with limited resources, including low RAM and CPU and battery capacity. As the process of iterative evolution progresses, each level demonstrates adaptability to the capabilities of various IoT devices. Moreover, the algorithm progresses towards its higher levels, which are characterised by complex equations, functions, output lengths, more words, increased arithmetic and logical operations and iterations. These advanced levels of the proposed algorithm were designed to cater to larger, more complex IoT devices than the initial-stage devices.
Downloads
References
M. Sathyan, ‘Industry 4.0: Industrial internet of things (IIOT)’, Advances in computers, vol. 117. no. 1, pp. 129–164, Elsevier, 2020.
M. Sudip et al., ‘Industrial Internet of Things for safety management applications: A survey’, IEEE Access, vol. 10, pp. 83415–83439, 2022.
L. David et al., ‘The industrial internet of things networking framework’, Industrial IoT Consortium (2021).
K. Halim et al., ‘IoT, IIoT, and cyber-physical systems integration’, Emergence of Cyber Physical System and IoT in Smart Automation and Robotics: Computer Engineering in Automation. Cham: Springer International Publishing, 2021, pp. 31–50.
A .A. Y. Fahad and A. Popa, ‘LMAAS-IoT: Lightweight multi-factor authentication and authorization scheme for real-time data access in IoT cloud-based environment’, J. Netw. Comput. Appl., vol. 192, p. 103177, 2021.
E. Abderrahmane and K. Maalmi, ‘A new Internet of Things architecture for real-time prediction of various diseases using machine learning on big data environment’, J. Big Data, vol. 6, pp. 1–25, 2019.
H. Yosra et al., ‘Big data and IoT-based applications in smart environments: A systematic review’, Comput. Sci. Rev., vol. 39, p. 100318.
Williams, Phillip, et al. ‘A survey on security in internet of things with a focus on the impact of emerging technologies’, Internet of Things, vol. 19, p. 100564, 2022.
T. Lo’ai et al., ‘IoT privacy and security: Challenges and solutions’, Applied Sciences, vol. 10, no. 12, p. 4102, 2020.
S. E. R. Kumar and E. A. Dash, ‘Unveiling the shadows: Exploring the security challenges of the Internet of Things (IoT)’, 2023.
L. V. Cherckesova et al., ‘Developing a new collision-resistant hashing algorithm’, Mathematics, vol. 10, no. 15, p. 2769, 2022.
A. Abouchouar and F. Omary, ‘New implementation of the abstract design for non-iterative hash functions with 1024 digest length NIHF-1024’, Int. J. Artif. Intell., vol. 11, no. 4, p. 1213, 2022.
M. Shah, ‘Secure hash algorithms for securing IoT’, Conference secretariat. p. 63. 2021.
T. Usman et al., ‘A critical cybersecurity analysis and future research directions for the Internet of Things: A comprehensive review’, Sensors, vol. 23, no. 8, p. 4117, 2023.
E. T. Michailidis and D. Vouyioukas, ‘A review on software-based and hardware-based authentication mechanisms for the Internet of Drones’, Drones, vol. 6, no. 2, p. 41, 2022.
P. Monika and H. J. Kaur, ‘Comparative analysis of secured hash algorithms for blockchain technology and Internet of Things’, Int J Adv Comput Sci Appl, vol. 12, no. 3, 2021.
R. Vidya and K. V. Prema, ‘Comparative study of lightweight hashing functions for resource constrained devices of IoT’, presented at the 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), IEEE, City, State, Country, Month days, 2019.
U. I. Khan et al., ‘Evolution and analysis of secured hash algorithm (SHA) family’, Malays. J. Comput. Sci., vol. 35, no. 3, pp. 179–200, 2022.
T. M. G. Sami, S. R. M. Zeebaree and S. H. Ahmed, ‘A comprehensive review of hashing algorithm optimization for IoT devices’, Int. J. Intell. Syst. Appl. Eng, vol. 11, no. 6s, pp. 205–231, 2023.
G. Sandip et al., ‘A journey from md5 to sha-3’, Trends in Communication, Cloud, and Big Data: Proceedings of 3rd National Conference on CCB, 2018, City, Singapore: Springer, 2020.
K. Sakan, S. Nyssanbayeva, N. Kapalova, K. Algazy, A. Khompysh and D. Dyusenbayev, ‘Development and analysis of the new hashing algorithm based on block cipher’, Eastern-European J. Enterp. Technol., vol. 2, pp. 60–73, 2022.
A. V. Tutueva, A. I. Karimov, L. Moysis, C. Volos, D. N. Butusov, ‘Construction of one-way hash functions with increased key space using adaptive chaotic maps’, Chaos Solit. Fractals, vol. 141, p. 110344, 2020
Y. Wang, L. Chen, X. Wang, G. Wu, K. Yu and T. Lu, ‘The design of keyed hash function based on CNN-MD structure’, Chaos Solit. Fractals, vol. 152, p. 111443, 2021.
E. V. V. Da Silva, ‘A New Architecture for Hash Functions’, In Proceedings of the Future Technologies Conference (FTC), Vancouver, BC, Canada, Nov. 28–29, 2021.
H. E. N. Liquan et al., ‘New hash function based on C-MD structure and chaotic neural network’, Chin. J. Netw. Inf. Secur. vol. 9, no. 3, 2023.
M. Al-Zubaidie. ‘Implication of lightweight and robust hash function to support key exchange in health sensor networks’, Symmetry, vol. 15, no. 1, p. 152, 2023.
S. M. Myint, S. Moe, M. M. Myint and A. A. Cho, ‘A study of SHA algorithm in cryptography’, Int. J. Trend Sci. Res. Dev, vol. 3, pp. 1453–1454, 2019.
S. U. A. Laghari et al, ‘ES-SECS/GEM: An efficient security mechanism for SECS/GEM communications’, IEEE Access, vol. 11, pp. 31813–31828, 2023.
S. A. A Hakeem, M. A. A. El-Gawad and H. Kim, ‘A decentralized lightweight authentication and privacy protocol for vehicular networks’, IEEE Access, vol. 7, pp. 119689–119705, 2019.
P. P. Pittalia, ‘A comparative study of hash algorithms in cryptography’. Int. J. Comput. Sci. Mob. Computing, vol. 8, no. 6, pp. 147–152, 2019.
X. Yi et al., Blockchain Foundations and Applications. Springer Nature, 2022.
O. Vashchuk and R. Shuwar, ‘Pros and cons of consensus algorithm proof of stake. Difference in the network safety in proof of work and proof of stake’, J. Electron. Inf. Tech, vol. 9, no. 9, pp. 106–112, 2018.
Z. Rong and W. K. V. Chan, ‘Evaluation of energy consumption in block-chains with proof of work and proof of stake’, J. Phys. Conf. Ser., vol. 1584, no. 1, 2020.
J. Guo et al., ‘Practical collision attacks against round-reduced SHA-3’, J. Cryptol., vol. 33, pp. 228–270, 2020.
S. Peng, S. Pal and L. Huang, Ed. Principles of Internet of Things (IoT) ecosystem: Insight paradigm. Springer International Publishing, 2020.
J. H. Nord, A. Koohang and J. Paliszkiewicz, ‘The Internet of Things: Review and theoretical framework’, Expert Syst. Appl., vol. 133, pp. 97–108, 2019.
S. E. Bibri, ‘The core academic and scientific disciplines underlying data-driven smart sustainable urbanism: An interdisciplinary and transdisciplinary framework’, Computational Urban Science, vol. 1, pp. 1–32, 2021.
Q. Chen, H. Shi and J. Chen, ‘Development management of infant dairy industry integrating Internet of Things under the background of family planning policy adjustment’, Secur. Commun. Netw., 2022.
Y. Khan et al., ‘Application of Internet of Things (IoT) in sustainable supply chain management’, Sustainability, vol. 15, no. 1, p. 694, 2022.
J. Jamali et al., ‘IoT architecture’, Towards the Internet of Things: Architectures, Security, and Applications, pp. 9–31, 2020.
Alejandro Garcia, Machine Learning for Customer Segmentation and Targeted Marketing , Machine Learning Applications Conference Proceedings, Vol 3 2023.
Begum, S. . S. ., Prasanth, K. D. ., Reddy, K. L. ., Kumar, K. S. ., & Nagasree, K. J. . (2023). RDNN for Classification and Prediction of Rock or Mine in Underwater Acoustics. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 98–104. https://doi.org/10.17762/ijritcc.v11i3.6326
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.