The Dark (and Bright) Side of IoT: Attacks and Countermeasures for Identifying Smart Home Devices and Services

TL;DR

This paper demonstrates that machine learning can identify smart home IoT devices and their services through network traffic analysis, exposing privacy vulnerabilities despite encryption, and proposes a mitigation technique called Eclipse.

Contribution

It introduces a novel machine learning attack for device detection and a mitigation method called Eclipse to protect user privacy in IoT environments.

Findings

Successful detection of devices with high probability

Traffic reshaping reduces identification accuracy to baseline levels

Encryption alone does not prevent device identification

Abstract

We present a new machine learning-based attack that exploits network patterns to detect the presence of smart IoT devices and running services in the WiFi radio spectrum. We perform an extensive measurement campaign of data collection, and we build up a model describing the traffic patterns characterizing three popular IoT smart home devices, i.e., Google Nest Mini, Amazon Echo, and Amazon Echo Dot. We prove that it is possible to detect and identify with overwhelming probability their presence and the services running by the aforementioned devices in a crowded WiFi scenario. This work proves that standard encryption techniques alone are not sufficient to protect the privacy of the end-user, since the network traffic itself exposes the presence of both the device and the associated service. While more work is required to prevent non-trusted third parties to detect and identify the user's devices, we introduce Eclipse, a technique to mitigate these types of attacks, which reshapes the traffic making the identification of the devices and the associated services similar to the random classification baseline.