Adaptive Chaotic-Neural Ascon Image Encryption with Mish Activation and AI-Driven Key Scheduling for Secure and High-Speed IoT Applications

Abstract

This paper presents a novel hybrid cryptosystem for image encryption that combines the lightweight Ascon authenticated cypher with neural networks and chaotic systems. The proposed Chaotic-Neural Ascon Image Encryption (CNAIE) system employs Mish activation functions in neural diffusion and reinforcement learning through Q-learning for key scheduling adaptability. Our approach addresses the urgent need for lightweight and secure encryption methods for Internet of Things (IoT) devices with minimal computational overhead. Experimental results on several test images demonstrate the proficiency of the cryptosystem with near-optimal encryption entropy ($\approx 7.99$) and negligible adjacent pixel correlation (<0.01) compared to plaintext images ($\gt0.90$). The uniform histogram distribution and randomised pixel relations within encrypted images confirm the resilience against statistical attacks. Security analysis confirms the algorithm’s sensitivity to minor key alterations, where changing a single bit causes drastically different outputs. Performance tests demonstrate the system’s feasibility in resource-constrained IoT environments with NIST-compliant security features.