Recent developments and emerging trends : Authentication and access control for mobile IoT devices using biometric features
Monika Vishwakarmamonika.vishwakarma@jaipur.manipal.eduDepartment of Computer ApplicationsManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Suparn Padma Patrasuparnpatra@gmail.comDepartment of Computer ScienceCentral University of RajasthanKishangarh, Rajasthan, 305817, IndiaView full profile → , *Pragya VaishnavCorresponding authorpragya.vaishnav@jaipur.manipal.eduDepartment of Computer ApplicationsManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 07 May 2025
- Published Online:
- 13 Feb 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-2529
- Pages:
- 827–837
Abstract
Keywords
Subject Classifications
References
[1] Wind River, “42 billion connected IoT devices by 2025: Will they be secure?” [Online]. Available: https://www.windriver.com/solutions/learning/security-briefings. Accessed: May 16, 2025.
[2] Kinetik Bilişim, “Industrial IoT security vulnerabilities and comprehensive solutions,” [Online]. Available: https://kinetikbilisim.net/en/industrial-iot-security-vulnerabilities-and-comprehensive-solutions/. Accessed: May 16, 2025.
[3] A. Alotaibi, H. Aldawghan, and A. Aljughaiman, “A review of the authentication techniques for Internet of Things devices in smart cities: Opportunities, challenges, and future directions,” Sensors, vol. 25, no. 6, pp. 1649 (2025).
[4] V. Singh and C. Kant, “Biometric-based authentication in Internet of Things (IoT): A review,” in Advances in Information Communication Technology and Computing: Proc. AICTC 2021, pp. 309–317 (2022).
[5] H. Alhakami, “Knowledge-based authentication techniques and challenges,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 2 (2020).
[6] A. Kumar, P. Dadheech, V. Singh, R. C. Poonia, and L. Raja, “An improved quantum key distribution protocol for verification,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 22, no. 4, pp. 491–498 (2019).
[7] M. Golec, S. S. Gill, R. Bahsoon, and O. Rana, “BioSec: A biometric authentication framework for secure and private communication among edge devices in IoT and Industry 4.0,” IEEE Consumer Electron. Mag., vol. 11, no. 2, pp. 51–56 (2020).
[8] A. A. Abbas and Z. M. Sallal, “Hybrid biometric authentication for bank security improvements,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 4-A, pp. 1037–1050 (2025), doi: 10.47974/JDMSC-2030.
[9] S. Dargan and M. Kumar, “A comprehensive survey on biometric recognition systems based on physiological and behavioral modalities,” Expert Syst. Appl., vol. 143, pp. 113114 (2020).
[10] H. Zhao and L. Njilla, “Hardware assisted chaos-based IoT authentication,” in Proc. IEEE 16th Int. Conf. Networking, Sensing and Control (ICNSC), pp. 169–174 (May 2019).
[11] S. Minaee, A. Abdolrashidi, H. Su, M. Bennamoun, and D. Zhang, “Biometric recognition using deep learning: A survey,” Artif. Intell. Rev., vol. 56, no. 8, pp. 8647–8695 (2023).
[12] A. Hussian, F. Murshed, M. N. Alandoli, and G. Aljafari, “A hybrid deep learning approach for secure biometric authentication using fingerprint data,” Computers, vol. 14, no. 5, pp. 178 (2025).
[13] A. Sardar, S. Umer, R. K. Rout, K. S. Sahoo, and A. H. Gandomi, “Enhanced biometric template protection schemes for securing face recognition in IoT environments,” IEEE Internet Things J., vol. 11, no. 13, pp. 23196–23206 (2024).
[14] B. M. Alsellami and P. D. Deshmukh, “The recent trends in biometric traits authentication based on Internet of Things (IoT),” in Proc. 2021 Int. Conf. Artificial Intelligence and Smart Systems (ICAIS), pp. 1359–1365, IEEE (2021).
[15] T. S. Sasikala, “A secure multimodal biometrics using deep ConvGRU neural networks-based hashing,” Expert Syst. Appl., vol. 235, pp. 121096 (2024).
[16] S. Umer, A. Sardar, R. K. Rout, M. Tanveer, and I. Razzak, “IoT-enabled multimodal biometric recognition system in secure environments,” IEEE Internet Things J., vol. 10, no. 24, pp. 21457–21466 (2023).
[17] S. H. G. Salem, A. Y. Hassan, M. S. Moustafa, and M. N. Hassan, “Blockchain-based biometric identity management,” Cluster Comput., vol. 27, no. 3, pp. 3741–3752 (2024).
[18] S. Maleki Lonbar, A. Beigi, N. Bagheri, P. Peris-Lopez, and C. Camara, “Deep learning-based biometric authentication system using a high temporal/frequency resolution transform,” Front. Digit. Health, vol. 6, pp. 1463713 (2024).
[19] K. K. Coelho, E. T. Tristão, M. Nogueira, A. B. Vieira, and J. A. Nacif, “Multimodal biometric authentication method by federated learning,” Biomed. Signal Process. Control, vol. 85, pp. 105022 (2023).
[20] M. B. Bilgen, O. Abul, and K. Bicakci, “Authentication-enabled attribute-based access control for smart homes,” Int. J. Inf. Secur., vol. 22, no. 2, pp. 479–495 (2023).
[21] I. U. Onwuegbuzie and O. A. Alabi, “A review of authentication and authorization mechanisms in zero trust architecture: Evolution and efficiency,” Tech-Sphere J. Pure Appl. Sci., vol. 2, no. 1 (2025).
[22] D. Alsadie, “Artificial intelligence techniques for securing fog computing environments: Trends, challenges, and future directions,” IEEE Access, vol. 12, pp. 51598–151648 (2024).
[23] B. Menakadevi, D. S. Kumar, P. Nagasaratha, and K. Parimalam, “Biometric system attacks: A case study,” in Proc. 2025 Int. Conf. Computer, Electrical & Communication Engineering (ICCECE), pp. 1–7 (Feb. 2025).
[24] J. Guo, H. Mu, X. Liu, H. Ren, and C. Han, “Federated learning for biometric recognition: A survey,” Artif. Intell. Rev., vol. 57, no. 8, pp. 208 (2024).
[25] S. P. Patra and M. Rani, “ChaoticRIPE: Strengthening RIPEMD-160 with the Chirikov standard map for enhanced cryptographic security,” in CS & IT Conf. Proc., vol. 15, no. 4, pp. 169–182 (2025).
[26] A. Altameem, P. P., R. C. Poonia, and A. K. J. Saudagar, “A hybrid AES with a chaotic map-based biometric authentication framework for IoT and Industry 4.0,” Systems, vol. 11, no. 1, pp. 28 (2023).




