@Article{jcs.2025.073690,
AUTHOR = {Mircea Ţălu},
TITLE = {Securing IoT Ecosystems: Experimental Evaluation of Modern Lightweight Cryptographic Algorithms and Their Performance},
JOURNAL = {Journal of Cyber Security},
VOLUME = {7},
YEAR = {2025},
NUMBER = {1},
PAGES = {565--587},
URL = {http://www.techscience.com/JCS/v7n1/64813},
ISSN = {2579-0064},
ABSTRACT = {The rapid proliferation of Internet of Things (IoT) devices has intensified the demand for cryptographic solutions that balance security, performance, and resource efficiency. However, existing studies often focus on isolated algorithmic families, lacking a comprehensive structural and experimental comparison across diverse lightweight cryptographic designs. This study addresses that gap by providing an integrated analysis of modern lightweight cryptographic algorithms spanning six structural classes—Substitution–Permutation Network (SPN), Feistel Network (FN), Generalized Feistel Network (GFN), Addition–Rotation–XOR (ARX), Nonlinear Feedback Shift Register (NLFSR), and Hybrid models—evaluated on resource-constrained IoT platforms. The key contributions include: (i) establishing a unified benchmarking framework based on standardized evaluation metrics (ROM/RAM usage, throughput, latency, and energy efficiency); (ii) conducting cross-platform experimental validation using Fair Evaluation of Lightweight Cryptographic Systems (FELICS) and the National Institute of Standards and Technology (NIST) reference implementations; and (iii) deriving performance–security trade-offs that map cipher structures to optimal IoT deployment tiers. Results demonstrate that SPN and ARX-based algorithms achieve the best balance between cryptographic strength and implementation efficiency, while Hybrid models exhibit superior adaptability across microcontroller architectures. The findings provide quantitative guidance for selecting lightweight cryptography aligned with hardware capabilities and threat profiles, thereby contributing to the design of scalable and energy-aware IoT security frameworks.},
DOI = {10.32604/jcs.2025.073690}
}