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

Eduardo Baena, Han Yang, Dimitrios Koutsonikolas, Israat Haque

TL;DR

The paper addresses security and privacy risks in smart-home IoT by surveying network-traffic-based fingerprinting for detection and prevention. It surveys device discovery, event inference, and policy enforcement techniques, alongside prevention methods (packet padding, traffic injection, traffic shaping) andEmerging GenAI-enabled approaches, emphasizing deployment-feasibility and data-feature considerations. Key contributions include a structured taxonomy, deployment-aware evaluation, and identification of gaps such as real-world longitudinal datasets, adversarial robustness, and cross-domain generalization. The findings underscore practical implications for operators and researchers and chart a path toward scalable, privacy-preserving fingerprinting and defense mechanisms in next-generation smart homes.

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.

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

TL;DR

The paper addresses security and privacy risks in smart-home IoT by surveying network-traffic-based fingerprinting for detection and prevention. It surveys device discovery, event inference, and policy enforcement techniques, alongside prevention methods (packet padding, traffic injection, traffic shaping) andEmerging GenAI-enabled approaches, emphasizing deployment-feasibility and data-feature considerations. Key contributions include a structured taxonomy, deployment-aware evaluation, and identification of gaps such as real-world longitudinal datasets, adversarial robustness, and cross-domain generalization. The findings underscore practical implications for operators and researchers and chart a path toward scalable, privacy-preserving fingerprinting and defense mechanisms in next-generation smart homes.

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.

Paper Structure

This paper contains 29 sections, 4 figures, 15 tables.

Table of Contents

  1. Introduction
  2. Scope and Methodology
  3. Scope of the Survey
  4. Methodology
  5. Paper Selection
  6. Related Work
  7. Taxonomy of IoT Fingerprint Detection
  8. Discovering IoT Devices in a Network
  9. Statistical Feature-based Fingerprinting
  10. Fingerprinting based on Categorical Features
  11. Fingerprinting based on Hybrid Features
  12. Inferring Events
  13. Identifying Device State Transitions
  14. Classifying Events and Profiling User Activities
  15. Enforcing Event-Level Policies
  16. ...and 14 more sections

Figures (4)

  • Figure 1: Each fingerprint captures the essence of network traffic characteristics specific to its respective device.
  • Figure 2: An IoT device can be in one of these three operational states.
  • Figure 3: Main defense approaches to prevent IoT fingerprinting, including packet padding, traffic injection, and traffic shaping.
  • Figure 4: An example of local and external adversaries.