A Lightweight Security Framework for Edge Layer IoT Networks using Neural Cryptography and Virtualization

Securing the edge layer is essential in modern cybersecurity architectures, particularly for the Internet of Things (IoT), where resource-constrained devices require robust yet lightweight protection mechanisms. This paper introduces a novel Neural Cryptography Secure Router (NCSR) framework that integrates Tree Parity Machine (TPM)-based neural key generation with AES encryption, OpenWRT-based firewalling, and a virtualized intrusion detection/prevention system. The architecture is implemented using Raspberry Pi devices at the edge and a Fedora-based host for virtualization and centralized security processing. The framework features two Raspberry Pi units: the first simulates an IoT node, encrypting sensor data with TPM-generated keys before transmission via SSH/SCP; the second operates as a secure router, running OpenWRT and nftables for real-time packet filtering. The Fedora host functions as a multi-layered security hub, hosting virtual machines (pfSense and Security Onion) for firewalling, deep packet inspection, and threat analysis via Snort and Suricata. This integrated model eliminates the need for pre-shared keys while ensuring end-to-end confidentiality and dynamic session key exchange. Empirical evaluations demonstrate strong performance with minimal resource consumption: 1.2 ms/KB encryption time, 1.1 ms/KB decryption time, 25% CPU utilization, 95.5% firewall drop efficiency, and a 7% false positive rate. Comparative analysis with existing solutions confirms the model’s advantages in terms of security, scalability, and computational efficiency, establishing NCSR as a practical and novel security solution for IoT edge networks.