A Secure Edge-AI-Enabled Smart Home Framework Using ASCON Lightweight Cryptography and Federated Learning for IoT Threat Detection

Abstract

The rapid growth of Internet of Things (IoT) devices in residential environments has transformed traditional homes into intelligent and automated ecosystems. While smart home systems improve convenience, energy efficiency, and remote accessibility, they also introduce critical cybersecurity challenges including unauthorized access, botnet attacks, privacy leakage, and insecure device communication. Traditional cloud-centric architectures often suffer from high latency, bandwidth dependency, and privacy risks, making them less suitable for modern resource-constrained IoT environments.
This paper proposes a secure edge-AI enabled smart home framework that integrates ASCON lightweight cryptography, Federated Learning (FL), and Matter protocol to provide secure, privacy-preserving, and interoperable communication. The proposed architecture consists of four layers: Perception Layer, Network Layer, Edge/Middleware Layer, and Application Layer. ASCON is employed to secure communication between constrained IoT devices with lower computational overhead than traditional AES-based encryption. For intelligent threat detection, a Federated Learning-based anomaly detection model is deployed at edge gateways to detect malicious behaviour without transmitting raw user data to centralized servers.
Performance evaluation is conducted using cryptographic benchmarking and machine learning metrics including encryption latency, throughput, memory usage, accuracy, F1-score, and false positive rate. Experimental analysis indicates that the integration of lightweight cryptography and edge-based federated intelligence significantly improves security, efficiency, and privacy for next-generation smart home ecosystems.