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Low-cost Influence-Limiting Defense against Adversarial Machine Learning Attacks in Cooperative Spectrum Sensing

Authors:

Shangqing Zhao,

Yalin E. Sagduyu,

Jie XuAuthors Info & Claims

WiseML '21: Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning

Pages 55 - 60

https://doi.org/10.1145/3468218.3469051

Published: 28 June 2021 Publication History

Abstract

Cooperative spectrum sensing aims to improve the reliability of spectrum sensing by individual sensors for better utilization of the scarce spectrum bands, which gives the feasibility for secondary spectrum users to transmit their signals when primary users remain idle. However, there are various vulnerabilities experienced in cooperative spectrum sensing, especially when machine learning techniques are applied. The influence-limiting defense is proposed as a method to defend the data fusion center when a small number of spectrum sensing devices is controlled by an intelligent attacker to send erroneous sensing results. Nonetheless, this defense suffers from a computational complexity problem. In this paper, we propose a low-cost version of the influence-limiting defense and demonstrate that it can decrease the computation cost significantly (the time cost is reduced to less than 20% of the original defense) while still maintaining the same level of defense performance.

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Cited By

Zhao WLi XZhao SXu JLiu YLu Z(2024)Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary StatisticsIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621153(691-700)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621153
Khalek NTashman DHamouda W(2024)Advances in Machine Learning-Driven Cognitive Radio for Wireless Networks: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.334579626:2(1201-1237)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2023.3345796
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
https://doi.org/10.1016/j.hcc.2024.100220
Show More Cited By

Index Terms

Low-cost Influence-Limiting Defense against Adversarial Machine Learning Attacks in Cooperative Spectrum Sensing
1. Networks
  1. Network properties
    1. Network reliability
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems

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cover image ACM Conferences

WiseML '21: Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning

June 2021

104 pages

ISBN:9781450385619

DOI:10.1145/3468218

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 28 June 2021

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National Science Foundation

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WiSec '21

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WiSec '21: 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks

June 28 - July 2, 2021

Abu Dhabi, United Arab Emirates

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Cited By

Zhao WLi XZhao SXu JLiu YLu Z(2024)Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary StatisticsIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621153(691-700)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621153
Khalek NTashman DHamouda W(2024)Advances in Machine Learning-Driven Cognitive Radio for Wireless Networks: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.334579626:2(1201-1237)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2023.3345796
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
https://doi.org/10.1016/j.hcc.2024.100220
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
https://dl.acm.org/doi/10.1109/TMC.2022.3148690
Xue JQu ZZhao SLiu YLu Z(2023)Data-Driven Next-Generation Wireless Networking: Embracing AI for Performance and Security2023 32nd International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN58024.2023.10230189(1-10)Online publication date: Jul-2023
https://doi.org/10.1109/ICCCN58024.2023.10230189
Adesina DHsieh CSagduyu YQian L(2023)Adversarial Machine Learning in Wireless Communications Using RF Data: A ReviewIEEE Communications Surveys & Tutorials10.1109/COMST.2022.320518425:1(77-100)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/COMST.2022.3205184
Kim BSagduyu YDavaslioglu KErpek TUlukus S(2022)Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal ClassifiersIEEE Transactions on Wireless Communications10.1109/TWC.2021.312485521:6(3868-3880)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1109/TWC.2021.3124855
Kim BShi YSagduyu YErpek TUlukus S(2021)Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications2021 IEEE Globecom Workshops (GC Wkshps)10.1109/GCWkshps52748.2021.9682097(1-6)Online publication date: Dec-2021
https://doi.org/10.1109/GCWkshps52748.2021.9682097

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