research-article

Adversarial machine learning based partial-model attack in IoT

Authors:

Shangqing Zhao,

Yalin E. Sagduyu,

Jie XuAuthors Info & Claims

WiseML '20: Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning

Pages 13 - 18

https://doi.org/10.1145/3395352.3402619

Published: 16 July 2020 Publication History

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.

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Index Terms

Adversarial machine learning based partial-model attack in IoT
1. Networks
  1. Network properties
    1. Network reliability
2. Security and privacy
  1. Network security
    1. Mobile and wireless security

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cover image ACM Conferences

WiseML '20: Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning

July 2020

91 pages

ISBN:9781450380072

DOI:10.1145/3395352

General Chairs:
René Mayrhofer
Johannes Kepler University Linz, Linz, AT
,
Michael Roland
Johannes Kepler University Linz, Linz, AT

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 16 July 2020

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WiSec '20: 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks

July 13, 2020

Linz, Austria

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Cited By

Khazane HRidouani MSalahdine FKaabouch N(2024)A Holistic Review of Machine Learning Adversarial Attacks in IoT NetworksFuture Internet10.3390/fi1601003216:1(32)Online publication date: 19-Jan-2024
https://doi.org/10.3390/fi16010032
Ojo SKrichen MAlamro MMihoub A(2024)TXAI-ADV: Trustworthy XAI for Defending AI Models against Adversarial Attacks in Realistic CIoTElectronics10.3390/electronics1309176913:9(1769)Online publication date: 3-May-2024
https://doi.org/10.3390/electronics13091769
Han DBabaei RZhao SCheng S(2024)Exploring the Efficacy of Learning Techniques in Model Extraction Attacks on Image Classifiers: A Comparative StudyApplied Sciences10.3390/app1409378514:9(3785)Online publication date: 29-Apr-2024
https://doi.org/10.3390/app14093785
MWANGI JKURIA JWANDETO J(2024)Block Switching: Defying Fast Gradient Sign Resistance2024 IST-Africa Conference (IST-Africa)10.23919/IST-Africa63983.2024.10569920(1-12)Online publication date: 20-May-2024
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Liu CChen BShao WZhang CWong KZhang Y(2024)Unraveling Attacks to Machine-Learning-Based IoT Systems: A Survey and the Open Libraries Behind ThemIEEE Internet of Things Journal10.1109/JIOT.2024.337773011:11(19232-19255)Online publication date: 1-Jun-2024
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Rachakonda LSiddula MSathya V(2024)A comprehensive study on IoT privacy and security challenges with focus on spectrum sharing in Next-Generation networks (5G/6G/beyond)High-Confidence Computing10.1016/j.hcc.2024.1002204:2(100220)Online publication date: Jun-2024
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Utomo SRouniyar AHsu HHsiung P(2023)Federated Adversarial Training Strategies for Achieving Privacy and Security in Sustainable Smart City ApplicationsFuture Internet10.3390/fi1511037115:11(371)Online publication date: 20-Nov-2023
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Alkadi SAl-Ahmadi SBen Ismail M(2023)Toward Improved Machine Learning-Based Intrusion Detection for Internet of Things TrafficComputers10.3390/computers1208014812:8(148)Online publication date: 27-Jul-2023
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Alkadi SAl-Ahmadi SIsmail M(2023)Better Safe Than Never: A Survey on Adversarial Machine Learning Applications towards IoT EnvironmentApplied Sciences10.3390/app1310600113:10(6001)Online publication date: 13-May-2023
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Shi YSagduyu Y(2023)Membership Inference Attack and Defense for Wireless Signal Classifiers With Deep LearningIEEE Transactions on Mobile Computing10.1109/TMC.2022.314869022:7(4032-4043)Online publication date: 1-Jul-2023
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