Things that Matter -- Identifying Interactions and IoT Device Types in Encrypted Matter Traffic
Kristopher Alex Schlett, Bela Genge, Savio Sciancalepore

TL;DR
This paper analyzes the security of the Matter IoT standard, revealing that encrypted traffic patterns can be exploited by passive attackers to identify device types and interactions with high accuracy, posing privacy risks.
Contribution
It provides the first systematic analysis of encrypted Matter traffic, demonstrating how metadata patterns can be used to infer device types and interactions, exposing privacy vulnerabilities.
Findings
Over 95% accuracy in identifying Matter interactions despite network issues
At least 88% accuracy in classifying device types from encrypted traffic
Passive traffic analysis poses significant privacy risks for IoT users
Abstract
Matter is the most recent application-layer standard for the Internet of Things (IoT). As one of its major selling points, Matter's design imposes particular attention to security and privacy: it provides validated secure session establishment protocols, and it uses robust security algorithms to secure communications between IoT devices and Matter controllers. However, to our knowledge, there is no systematic analysis investigating the extent to which a passive attacker, in possession of lower layer keys or exploiting security misconfiguration at those layers, could infer information by passively analyzing encrypted Matter traffic. In this paper, we fill this gap by analyzing the robustness of the Matter IoT standard to encrypted traffic analysis performed by a passive eavesdropper. By using various datasets collected from real-world testbeds and simulated setups, we identify patterns…
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Taxonomy
TopicsInternet Traffic Analysis and Secure E-voting · Security and Verification in Computing · Advanced Malware Detection Techniques
