Artificial Intelligence-Based Intrusion Detection Systems for Smart Home Security
Abstract
The rapid adoption of Internet of Things (IoT) devices in residential settings, smart cameras, voice assistants, locks, thermostats, and appliances, has transformed the modern household into a densely networked environment, while simultaneously expanding the attack surface available to malicious actors. Traditional signature-based intrusion detection systems (IDS) struggle to keep pace with novel and evolving threats in this environment, motivating a shift toward artificial intelligence (AI) and machine learning (ML) based detection approaches capable of identifying both known and previously unseen attack patterns. This paper surveys the state of the art in AI-based intrusion detection for smart home security, covering system architectures, supervised and unsupervised learning techniques, deep learning and hybrid detection models, and the major threat vectors facing residential IoT networks, including botnet-driven distributed denial-of-service (DDoS) attacks, unauthorized access, and device spoofing. We examine representative implementations, including an early embedded System-on-Chip-based household intrusion prevention system, alongside more recent federated and deep-learning-based approaches, and discuss the practical constraints of deploying AI-based detection on resource-limited home hardware. The paper concludes by identifying open challenges in adversarial robustness, dataset generalizability, privacy-preserving detection, and interoperability across heterogeneous…
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