Adversarial machine learning based partial-model attack in IoT

Zhengping Luo, Shangqing Zhao, Zhuo Lu, Yalin E. Sagduyu, Jie Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationWiseML 2020 - Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning
PublisherAssociation for Computing Machinery
Pages13-18
Number of pages6
ISBN (Electronic)9781450380072
DOIs
StatePublished - Jul 13 2020
Event2nd ACM Workshop on Wireless Security and Machine Learning, WiseML 2020 - Linz, Virtual, Austria
Duration: Jul 13 2020 → …

Publication series

NameWiseML 2020 - Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning

Conference

Conference2nd ACM Workshop on Wireless Security and Machine Learning, WiseML 2020
CountryAustria
CityLinz, Virtual
Period7/13/20 → …

Keywords

  • adversarial machine learning
  • data fusion
  • internet of things
  • machine learning
  • wireless security

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Artificial Intelligence

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