Optimized Lightweight Cryptography for Two-Stage Adaptive Design under Reflected Normal Loss Function in the Internet of Things: Integration of Regression Models and Elliptic Curve Encryption
Hassan Mazarei
1
(
Department of Basic Sciences, Bushehr Branch, Islamic Azad University, Bushehr, Iran
)
Keywords: Internet of Things (IoT), Reflected Normal Loss Function, Linear Regression, Security, Lightweight Cryptography, Elliptic Curve Cryptography (ECC).,
Abstract :
The Internet of Things (IoT) plays a pivotal role in bridging the physical and digital worlds through billions of interconnected devices. However, its security challenges, particularly in data confidentiality and integrity, demand efficient solutions. This study proposes an optimized lightweight cryptographic method for IoT, leveraging two-stage adaptive design under Reflected Normal Loss Function (RNL). Statistical models were employed to analyze the relationship between key variables (key size, energy consumption, and execution time), and the RNL function was utilized to optimize the security-efficiency trade-off in resource-constrained IoT environments. The proposed method integrates an enhanced Blowfish algorithm with parallelization of the F function, achieving a 35–40% reduction in execution time, and combines it with Elliptic Curve Cryptography (ECC) for secure key management. To ensure data integrity, the lightweight BLAKE2s hash function replaces the insecure MD5 algorithm. Simulations using the iFogSim tool demonstrated that the proposed approach reduces energy consumption by 18–22% and execution time by 20–25% compared to traditional algorithms such as RSA and AES-128 in ECB mode, without imposing significant computational overhead on IoT devices. Additionally, replacing RSA with the lightweight PRESENT algorithm enhances resistance against side-channel attacks. Aligned with the hardware limitations of IoT nodes (e.g., limited processing power and battery life), this framework adheres to the hybrid ECIES standard (RFC 6090) for secure key exchange and employs linear regression models to reduce the probability of successful brute-force attacks to below 0.1%.
[1] Tun, S. Y. Y., Madanian, S., & Mirza, F. (2021). Internet of things (IoT) applications for elderly care: a reflective review. Aging clinical and experimental research, 33(4), 855-867.
[2] Alessio Botta, Walter de Donato, Valerio Persico, Antonio Pescap´e. (2019). integration of cloud computing and internet of things: a servey. Preprint submitted to Journal of Future Generation Computer Systems September.
[3] Khambra, Deepika. Dabas, Poonam. (2017). Secure Data Transmission using AES in IoT. International Journal of Application or Innovation in Engineering & Management (IJAIEM).
[4] Okello, Wanican Julian. Qingling Liu. Faizan Ali Siddiqui. Chaozhu Zhang1. (2017). A Survey of the Current State of Lightweight Cryptography for the Internet of Things. IEEE.
[5] Maple, Carsten. (2017). Security and privacy in the internet of things. JOURNAL OF CYBER POLICY. 2(2).
[6] Vasilomanolakis, Emmanoui. Jörg Daubert. Manisha Luthra. Evangelos Gazis. (2019). On the Security and Privacy of Internet of Things Architectures and Systems. INTERNATIONAL WORKSHOP ON SECURE INTERNET OF THINGS.
[7] Gope, Prosanta. Hwang, Tzonelih. (2020). A realistic lightweight authentication protocol preserving strong anonymity for securing RFID system. Journal computers & security. Elsevier.
[8] Gangireddy, V. K. R., Kannan, S., & Subburathinam, K. (2021). Implementation of enhanced blowfish algorithm in cloud environment. Journal of Ambient Intelligence and Humanized Computing, 12(3), 3999-4005.
[9] Cordova, R. S., Maata, R. L. R., & Halibas, A. S. (2019, November). Blowfish Algorithm Implementation on Electronic Data in a Communication Network. In 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA) (pp. 1-4). IEEE.
[10] Quilala, T. F. G., Sison, A. M., & Medina, R. P. (2018). Modified blowfish algorithm. Indones. J. Electr. Eng. Comput. Sci, 11(3), 1027-1034.
[11] Chandra, S., Paira, S., Alam, S. S., & Sanyal, G. (2014, November). A comparative survey of symmetric and asymmetric key cryptography. In 2014 international conference on electronics, communication and computational engineering (ICECCE) (pp. 83-93). IEEE.
[12] Wollinger, T., Pelzl, J., Wittelsberger, V., Paar, C., Saldamli, G., & Koç, Ç. K. (2004). Elliptic and hyperelliptic curves on embedded μP. ACM Transactions on Embedded Computing Systems (TECS), 3(3), 509-533.
[13] Kapoor, V., Abraham, V. S., & Singh, R. (2008). Elliptic curve cryptography. Ubiquity, 2008(May), 1-8.
[14] Medileh, S., Laouid, A., Euler, R., Bounceur, A., Hammoudeh, M., AlShaikh, M., ... & Khashan, O. A. (2020). A flexible encryption technique for the internet of things environment. Ad Hoc Networks, 106, 102240.
[15] Bhattasali, T. (2013). Licrypt: Lightweight cryptography technique for securing smart objects in internet of things environment. CSI Communications, 26-36.
[16] Yao, X., Chen, Z., & Tian, Y. (2015). A lightweight attribute-based encryption scheme for the Internet of Things. Future Generation Computer Systems, 49, 104-112.
[17] Rao, V., & Prema, K. V. (2021). A review on lightweight cryptography for Internet-of-Things based applications. Journal of Ambient Intelligence and Humanized Computing, 12, 8835-8857.
[18] Gupta, D. N., Kumar, R., & Kumar, A. (2020). Efficient encryption techniques for data transmission through the internet of things devices. In IoT and Cloud Computing Advancements in Vehicular Ad-Hoc Networks (pp. 203-228). IGI global.
[19] Xue, W., Luo, C., Shen, Y., Rana, R., Lan, G., Jha, S., ... & Hu, W. (2020). Towards a compressive-sensing-based lightweight encryption scheme for the Internet of Things. IEEE Transactions on Mobile Computing, 20(10), 3049-3065.
[20] Rana, M., Mamun, Q., and Islam, R. (2022). "Lightweight cryptography in IoT networks: A survey." Future Generation Computer Systems, 129, 77-89.
[21] Zhang, X., Tang, S., Li, T., Li, X., Wang, C. (2023). "GFRX: A New Lightweight Block Cipher for Resource-Constrained IoT Nodes." Electronics, 12, 405.
[22] Lightweight Cryptography for Internet of Things: A Review (2024). EAI Endorsed Transactions on Internet of Things.
[23] Biswas, A., Majumdar, A., Nath, S., et al. (2023). "LRBC: A Lightweight Block Cipher Design for Resource Constrained IoT Devices." Journal of Ambient Intelligence and Humanized Computing, 14.
[24] Diro, A., Reda, H., Chilamkurti, N., Mahmood, A., Zaman, N., & Nam, Y. (2020). Lightweight authenticated-encryption scheme for internet of things based on publish-subscribe communication. IEEE
[25] Beg, A., Al-Kharobi, T., & Al-Nasser, A. (2019, May). Performance evaluation and review of lightweight cryptography in an internet-of-things environment. In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1-6). IEEE.
[26] Spiring, F. (1993). The reflected normal loss function. Canad. J. Statist. 21(1):321–330.
[27] -Spiring, F. A., Yeung, A. S. (1998). A general class of loss functions with industrial applications. J. Qual. Technol. 30:152–162.