Adversarial Machine Learning Based Partial-model Attack in IoT
Document Type
Conference Proceeding
Publication Date
2020
Digital Object Identifier (DOI)
https://doi.org/10.1145/3395352.3402619
Abstract
As Internet of Things (IoT) has emerged as the next logical stage of the Internet, it has become imperative to understand the vulnerabilities of the IoT systems when supporting diverse applications. Because machine learning has been applied in many IoT systems, the security implications of machine learning need to be studied following an adversarial machine learning approach. In this paper, we propose an adversarial machine learning based partial-model attack in the data fusion/aggregation process of IoT by only controlling a small part of the sensing devices. Our numerical results demonstrate the feasibility of this attack to disrupt the decision making in data fusion with limited control of IoT devices, e.g., the attack success rate reaches 83% when the adversary tampers with only 8 out of 20 IoT devices. These results show that the machine learning engine of IoT system is highly vulnerable to attacks even when the adversary manipulates a small portion of IoT devices, and the outcome of these attacks severely disrupts IoT system operations.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Proceedings of the WiseML '20: Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning, New York, New York, July 2020, p. 13-18
Scholar Commons Citation
Luo, Zhengping; Zhao, Shangqing; Lu, Zhuo; Sagduyu, Yalin Evren; and Xu, Jie, "Adversarial Machine Learning Based Partial-model Attack in IoT" (2020). Electrical Engineering Faculty Publications. 42.
https://digitalcommons.usf.edu/ege_facpub/42
