Paper #4

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

Kavita Agrawal, Padala Prasad Reddy and Suresh Chittineni

Abstract: 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.

Keywords: AES Encryption; Edge Layer Security; Firewall; Intrusion Detection and Prevention System (IDS/IPS); Neural Cryptography.