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Export Reference (APA)
Sohail, H., Noetzold, D. & Leithardt, V. R. Q. (2026). PRISEC III: Dynamic cryptographic adaptation for balancing performance and security . Journal of Internet Services and Applications. 17 (1), 174-191
Export Reference (IEEE)
H. Sohail et al.,  "PRISEC III: Dynamic cryptographic adaptation for balancing performance and security ", in Journal of Internet Services and Applications, vol. 17, no. 1, pp. 174-191, 2026
Export BibTeX
@article{sohail2026_1784667270830,
	author = "Sohail, H. and Noetzold, D. and Leithardt, V. R. Q.",
	title = "PRISEC III: Dynamic cryptographic adaptation for balancing performance and security ",
	journal = "Journal of Internet Services and Applications",
	year = "2026",
	volume = "17",
	number = "1",
	doi = "10.5753/jisa.2026.6502",
	pages = "174-191",
	url = "https://journals-sol.sbc.org.br/index.php/jisa/about"
}
Export RIS
TY  - JOUR
TI  - PRISEC III: Dynamic cryptographic adaptation for balancing performance and security 
T2  - Journal of Internet Services and Applications
VL  - 17
IS  - 1
AU  - Sohail, H.
AU  - Noetzold, D.
AU  - Leithardt, V. R. Q.
PY  - 2026
SP  - 174-191
SN  - 1867-4828
DO  - 10.5753/jisa.2026.6502
UR  - https://journals-sol.sbc.org.br/index.php/jisa/about
AB  - This paper introduces PRISEC III, a dynamic cryptographic framework designed to balance security and performance in heterogeneous IoT environments. Unlike static, one-size-fits-all approaches, PRISEC III employs a role-based multi-level model that adapts cryptographic strategies according to data sensitivity, device constraints, and network conditions. The framework integrates lightweight symmetric primitives for efficiency, robust asymmetric methods for secure key exchange, and hybrid schemes that combine multiple layers of protection. To enhance long-term resilience, PRISEC III also incorporates post-quantum options, ensuring preparedness against emerging cryptographic threats. Evaluation results demonstrate that the approach achieves scalable security while maintaining feasible computational costs, making it suitable for large-scale and resource-constrained IoT deployments.
ER  -