Transformer-Guided Chaotic Neural Encryption Using Swish and GELU-Based Dynamic S-Boxes for Lightweight ASCON in Smart IoT Systems

Abstract

This paper advances a novel encryption scheme using the integration of transformer neural networks and chaotic systems to enhance security for resource-constrained IoT devices. A novel lightweight realization of the ASCON authenticated encryption scheme with dynamically generated S-boxes inspired by Swish and GELU neural activation functions stimulated by a transformer architecture is proposed. The scheme employs a Piecewise Linear Chaotic Map (PWLCM) with the Lyapunov exponent of 1.25 to generate pseudorandom sequences for key derivation. Experimental evidence gives high-quality cryptographic characteristics with entropies of 7.997 (close to theoretical maximum), correlation coefficients reduced to near-zero (0.004-0.02), and best diffusion properties (NPCR $\sim 99.6 \%$, UACI $\sim 31.4 \%$). Both the Swish and GELU-based S-boxes have near-ideal nonlinearity ($\sim 3.99$) as well as bit change ratios ($\sim 0.5$). Performance analysis shows modest resource requirements (0.105 MB additional memory) at a rate of $\sim \mathbf{0. 1 1 ~ M B} / \mathbf{s}$, thereby rendering the system viable for security-concerned IoT systems where security demands overtake processing demands. Relative comparison with AES-like and reduced chaotic algorithms validates that our approach maintains comparable security statistics while presenting greater immunity against potential future attacks due to its neural-based mechanism. The proposed system is a paradigm shift in adaptive cryptographic protection for future IoT systems.