Light Auditor: Power Measurement Can Tell Private Data Leakage Through IoT Covert Channels

Authors

Woosub Jung
Kailai Cui
Kenneth Koltermann
Junjie Wang
ChunSheng Xin, Old Dominion UniversityFollow
Gang Zhou

Document Type

Conference Paper

Publication Date

2023

DOI

10.1145/3560905.3568535

Publication Title

SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

Pages

518-532

Conference Name

SenSys '22: The 20th ACM Conference on Embedded Networked Sensor Systems, November 6-9, 2022, Boston, Massachusetts

Abstract

Despite many conveniences of using IoT devices, they have suffered from various attacks due to their weak security. Besides well-known botnet attacks, IoT devices are vulnerable to recent covert-channel attacks. However, no study to date has considered these IoT covert-channel attacks. Among these attacks, researchers have demonstrated exfiltrating users' private data by exploiting the smart bulb's capability of infrared emission.

In this paper, we propose a power-auditing-based system that defends the data exfiltration attack on the smart bulb as a case study. We first implement this infrared-based attack in a lab environment. With a newly-collected power consumption dataset, we pre-process the data and transform them into two-dimensional images through Continous Wavelet Transformation (CWT). Next, we design a two-dimensional convolutional neural network (2D-CNN) model to identify the CWT images generated by malicious behavior. Our experiment results show that the proposed design is efficient in identifying infrared-based anomalies: 1) With much fewer parameters than transfer-learning classifiers, it achieves an accuracy of 88% in identifying the attacks, including unseen patterns. The results are similarly accurate as the sophisticated transfer-learning CNNs, such as AlexNet and GoogLeNet; 2) We validate that our system can classify the CWT images in real time.

Rights

© 2022 Copyright held by the owner/authors.

This work is licensed under a Creative Commons Attribution International 4.0 License (CC BY 4.0).

Original Publication Citation

Jung, W., Cui, K., Koltermann, K., Wang, J., Xin, C., & Zhou, G. (2023). Light auditor: Power measurement can tell private data leakage through IoT covert channels. In SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems (pp. 518-532). Association for Computing Machinery. https://doi.org/10.1145/3560905.3568535

Repository Citation

Jung, Woosub; Cui, Kailai; Koltermann, Kenneth; Wang, Junjie; Xin, ChunSheng; and Zhou, Gang, "Light Auditor: Power Measurement Can Tell Private Data Leakage Through IoT Covert Channels" (2023). Electrical & Computer Engineering Faculty Publications. 351.
https://digitalcommons.odu.edu/ece_fac_pubs/351