Paper accepted at ACNS ’21. Best student paper award.
ZLeaks: Passive Inference Attacks on Zigbee based Smart Homes
Narmeen Shafqat (Northeastern University), Daniel J. Dubois (Northeastern University), David Choffnes (Northeastern University), Aaron Schulman (University of California San Diego), Dinesh Bharadia (University of California San Diego), Aanjhan Ranganathan (Northeastern University)
Last updated: 06/30/2022
ABSTRACT
Zigbee is an energy-efficient wireless IoT protocol that is increasingly being deployed in smart home settings. In this work, we analyze the privacy guarantees of Zigbee protocol. Specifically, we present ZLeaks, a tool that passively identifies in-home devices or events from the encrypted Zigbee traffic by 1) inferring a single application layer (APL) command in the event’s traffic, and 2) exploiting the device’s periodic reporting pattern and interval. This enables an attacker to infer user’s habits or determine if the smart home is vulnerable to unauthorized entry. We evaluated ZLeaks’ efficacy on 19 unique Zigbee devices across several categories and 5 popular smart hubs in three different scenarios; controlled RF shield, living smart-home IoT lab, and third-party Zigbee captures. We were able to i) identify unknown events and devices (without a-priori device signatures) using command inference approach with 83.6% accuracy, ii) automatically extract device’s reporting signatures, iii) determine known devices using the reporting signatures with 99.8% accuracy, and iv) identify APL commands in a public capture with 91.2% accuracy. In short, we highlight the trade-off between designing a lowpower, low-cost wireless network and achieving privacy guarantees. We have also released ZLeaks tool for the benefit of the research community.
ABOUT THIS PUBLICATION
Paper title: ZLeaks: Passive Inference Attacks on Zigbee based Smart Homes
Authors: Narmeen Shafqat (Northeastern University), Daniel J. Dubois (Northeastern University), David Choffnes (Northeastern University), Aaron Schulman (University of California San Diego), Dinesh Bharadia (University of California San Diego), Aanjhan Ranganathan (Northeastern University)
Full Text (PDF): available.
Citation:
@inproceedings{shafqat2022zleaks,
title={ZLeaks: Passive Inference Attacks on Zigbee based Smart Homes},
author={Shafqat, Narmeen and Dubois, Daniel J. and Choffnes, David and Schulman, Aaron and Bharadia, Dinesh and Ranganathan, Aanjhan},
booktitle={Proc. of the International Conference on Applied Cryptography and Network Security},
year={2022}
}