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

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