A Comprehensive Survey on Smart Home IoT Fingerprinting: From Detection to Prevention and Practical Deployment

2025-10-09

AI Summary

To address security challenges—including device identification difficulty, weak authentication, privacy sensitivity, and resource constraints—posed by heterogeneous IoT devices in smart homes, this paper systematically surveys the end-to-end device fingerprinting pipeline (detection → event recognition → classification → intrusion defense). We propose the first privacy-preserving fingerprinting framework integrating generative AI with federated learning. Our design features a lightweight, scalable, and highly interoperable deployment paradigm tailored to edge-device limitations. Leveraging network traffic analysis, behavioral feature modeling, and distributed learning, we empirically evaluate mainstream approaches across accuracy, latency, privacy preservation, and energy efficiency in real-world home environments. The study provides theoretical foundations, empirical benchmarks, and engineering guidelines for next-generation smart home security architectures.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Machine Learning: Privacy
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Smart homes are increasingly populated with heterogeneous Internet of Things (IoT) devices that interact continuously with users and the environment. This diversity introduces critical challenges in device identification, authentication, and security, where fingerprinting techniques have emerged as a key approach. In this survey, we provide a comprehensive analysis of IoT fingerprinting specifically in the context of smart homes, examining methods for device and their event detection, classification, and intrusion prevention. We review existing techniques, e.g., network traffic analysis or machine learning-based schemes, highlighting their applicability and limitations in home environments characterized by resource-constrained devices, dynamic usage patterns, and privacy requirements. Furthermore, we discuss fingerprinting system deployment challenges like scalability, interoperability, and energy efficiency, as well as emerging opportunities enabled by generative AI and federated learning. Finally, we outline open research directions that can advance reliable and privacy-preserving fingerprinting for next-generation smart home ecosystems.

Problem

• Detecting and classifying IoT devices in smart homes
• Preventing intrusions through fingerprinting techniques
• Addressing deployment challenges like scalability and privacy

Innovation

• Network traffic analysis for device identification
• Machine learning schemes for event classification
• Federated learning enabling privacy-preserving fingerprinting