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The number of smart home IoT (Internet of Things) devices has been growing fast in recent years. Along with the great benefits brought by smart home devices, new threats have appeared. One major threat to smart home users is the compromise of their privacy by traffic analysis (TA) attacks. Researchers have shown that TA attacks can be performed successfully on either plain or encrypted traffic to identify smart home devices and infer user activities. Tunneling traffic is a very strong countermeasure to existing TA attacks. However, in this work, we design a Signature based Tunneled Traffic Analysis (STTA) attack that can be effective even on tunneled traffic. Using a popular smart home traffic dataset, we demonstrate that our attack can achieve an 83% accuracy on identifying 14 smart home devices. We further design a simple defense mechanism based on adding uniform random noise to effectively protect against our TA attack without introducing too much overhead. We prove that our defense mechanism achieves approximate differential privacy.
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ChatterHub: Privacy Invasion via Smart Home Hub
Smart-home devices promise to make users’ lives more convenient. However, at the same time, such devices increase the possibility of breaching users’ privacy as they are tightly connected to the users’ daily lives and activities. To address privacy invasion through smart-home devices, we present ChatterHub. This novel approach accurately identifies smart-home devices’ activities with minimal monitoring of encrypted traffic in the home network. ChatterHub targets devices that can only connect to the Internet through a centralized smart-home hub (e.g., Samsung SmartThings) using Zigbee or Z-wave. Specifically, ChatterHub passively eavesdrops on encrypted network traffic from the hub and leverages machine learning techniques to classify events and states of smart-home devices. Using ChatterHub, an adversary can identify smart-home devices’ specific activities without prior knowledge of the target smart home (e.g., list of deployed devices, types of communication protocols). We evaluated the accuracy and efficiency of ChatterHub in three real-world smart-home environments, and the evaluation results show that an attacker can successfully disclose smart-home devices’ behaviors with over 88% F1 score. We further demonstrate that ChatterHub successfully recognizes privacy-sensitive activities, including open and close of a smart door lock and turn on and off of smart LED. Additionally, to mitigate the threats posed by ChatterHub, we introduce two approaches, packet padding and random sequence injection. These mitigation approaches can effectively prevent threats from ChatterHub with only 9.2MB of additional network traffic per day.
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- Award ID(s):
- 1916550
- PAR ID:
- 10298285
- Date Published:
- Journal Name:
- Proceedings of the 2021 IEEE Conference on Smart Computing (SmartComp)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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