ANEF: Adversarial Neural Encryption Framework for Secured Consumer Electronics in Smart Cities

Secure communication in resource-constrained Internet of Things (IoT) deployments—such as consumer electronics and smart city infrastructures—faces persistent challenges from both limited processing capacity and increasingly adaptive cyber threats. This work introduces the Adversarial Neural Encryption Framework (ANEF), which models encryption as a non-invertible mapping trained through an adversarial three-agent setup comprising an encoder (Alice), a decoder (Bob), and an adversary (Eve). The framework integrates entropy-regularized encryption, dynamic key scheduling with periodic reseeding, and quantization-aware optimization to support 8-bit inference on constrained hardware. Adversarial resistance is reinforced through residual masking and curvature-based penalties, improving robustness against adaptive attacks. Training is guided by a hybrid objective that balances reconstruction fidelity, orthogonality preservation, and adversarial minimization, applied over sequential encryption states. Experiments using the UNSW-NB15 dataset evaluate accuracy, communication overhead, and adversary success rate across diverse packet sizes. ANEF achieves 94.7% decryption accuracy with a 10.3% transmission overhead, while maintaining a 4.1% adversary success rate.