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The Forest Behind the Tree: Revealing Hidden Smart Home Communication Patterns

François De Keersmaeker, Rémi Van Boxem, Cristel Pelsser, Ramin Sadre

TL;DR

The paper tackles the problem of incomplete profiling of Smart Home device network behavior by focusing on hidden communication patterns that emerge when default traffic fails. It introduces a largely automated framework that actively disturbs device communication, blocks targeted flows, and constructs multi-level event signature trees to reveal additional flows. Applied to ten real devices, the approach discovers 254 unique flows, including 70 hidden patterns, and introduces a robustness score to quantify backup strategies, with open-source tooling for replication. This method enables more accurate and resilient security configurations for home networks and provides deeper insights into device robustness under network perturbations.

Abstract

The widespread use of Smart Home devices has attracted significant research interest in understanding their behavior within home networks. Unlike general-purpose computers, these devices exhibit relatively simple and predictable network activity patterns. However, previous studies have primarily focused on normal network conditions, overlooking potential hidden patterns that emerge under challenging conditions. Discovering these hidden flows is crucial for assessing device robustness. This paper addresses this gap by presenting a framework that systematically and automatically reveals these hidden communication patterns. By actively disturbing communication and blocking observed traffic, the framework generates comprehensive profiles structured as behavior trees, uncovering flows that are missed by more shallow methods. This approach was applied to ten real-world devices, identifying 254 unique flows, with over 27% only discovered through this new method. These insights enhance our understanding of device robustness and can be leveraged to improve the accuracy of network security measures.

The Forest Behind the Tree: Revealing Hidden Smart Home Communication Patterns

TL;DR

The paper tackles the problem of incomplete profiling of Smart Home device network behavior by focusing on hidden communication patterns that emerge when default traffic fails. It introduces a largely automated framework that actively disturbs device communication, blocks targeted flows, and constructs multi-level event signature trees to reveal additional flows. Applied to ten real devices, the approach discovers 254 unique flows, including 70 hidden patterns, and introduces a robustness score to quantify backup strategies, with open-source tooling for replication. This method enables more accurate and resilient security configurations for home networks and provides deeper insights into device robustness under network perturbations.

Abstract

The widespread use of Smart Home devices has attracted significant research interest in understanding their behavior within home networks. Unlike general-purpose computers, these devices exhibit relatively simple and predictable network activity patterns. However, previous studies have primarily focused on normal network conditions, overlooking potential hidden patterns that emerge under challenging conditions. Discovering these hidden flows is crucial for assessing device robustness. This paper addresses this gap by presenting a framework that systematically and automatically reveals these hidden communication patterns. By actively disturbing communication and blocking observed traffic, the framework generates comprehensive profiles structured as behavior trees, uncovering flows that are missed by more shallow methods. This approach was applied to ten real-world devices, identifying 254 unique flows, with over 27% only discovered through this new method. These insights enhance our understanding of device robustness and can be leveraged to improve the accuracy of network security measures.

Paper Structure

This paper contains 28 sections, 1 equation, 11 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Problem statement and scope
  3. Profiling methodology
  4. General workflow
  5. Step 1: Traffic capturing
  6. Step 2: Event signature extraction
  7. Replacing IP addresses by domain names
  8. Aggregating packets to Flow IDs
  9. Building the event signature
  10. Step 3: Blocking flows with the event signature tree
  11. Tree creation
  12. Tree traversal and pruning
  13. How to block packets matching a Flow ID
  14. Experimental results
  15. Experiment setup
  16. ...and 13 more sections

Figures (11)

  • Figure 1: Typical Smart Home network with a smart lamp
  • Figure 2: Overview of the profiling workflow
  • Figure 3: Node pruning. The nodes Ⓒ, Ⓔ, and Ⓕ are not further explored (✗) in the BFS traversal because nodes with the same Flow ID have been already seen (nodes Ⓑ, Ⓐ, and Ⓓ, respectively).
  • Figure 4: Experimental Smart Home network.
  • Figure 5: Event signature tree for the TP-Link HS110's toggle event. $\leftrightarrow$ (resp. $\rightarrow$) denotes bidirectional (resp. unidirectional) flows. When indicated, the port is associated with its host.
  • ...and 6 more figures