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Poisoning Attack Anticipation in Mobile Crowdsensing: A Competitive Learning-Based Study

Published: 28 June 2021 Publication History

Abstract

Mobile Crowdsensing is prone to adversarial attacks particularly the data injection attacks to mislead the servers in the decision-making process. This paper aims to tackle the problem of threat anticipation from the standpoint of data poisoning attacks, and aims to model various classifiers to model the behaviour of the adversaries in a Mobile Crowdsensing setting. To this end, we study and quantify the impact of competitive learning-based data poisoning in a Mobile Crowdsensing environment by considering a black-box attack through a self organizing map. Under various machine learning classifiers in the decision-making platforms, it has been shown that the accuracy of the crowdsensing platform decisions are prone to a decrease in the range of 18%-22% when an adversary pursues a competitive learning-based data poisoning attack on the crowdsensing platform. Furthermore, we also show the robustness of certain classifiers under increasing poisoned samples.

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Cited By

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  • (2024)Systematic survey on artificial intelligence based mobile crowd sensing and sourcing solutions: Applications and security challengesAd Hoc Networks10.1016/j.adhoc.2024.103634164(103634)Online publication date: Nov-2024
  • (2023)On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based ApproachIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.32932698:4(540-554)Online publication date: Oct-2023
  • (2023)Utility-Aware Legitimacy Detection of Mobile Crowdsensing Tasks via Knowledge-Based Self Organizing Feature MapIEEE Transactions on Mobile Computing10.1109/TMC.2021.313623622:6(3706-3723)Online publication date: 1-Jun-2023
  • Show More Cited By

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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
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 the author(s) 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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Author Tags

  1. attack models
  2. machine learning
  3. mobile crowdsensing
  4. security

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Cited By

View all
  • (2024)Systematic survey on artificial intelligence based mobile crowd sensing and sourcing solutions: Applications and security challengesAd Hoc Networks10.1016/j.adhoc.2024.103634164(103634)Online publication date: Nov-2024
  • (2023)On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based ApproachIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.32932698:4(540-554)Online publication date: Oct-2023
  • (2023)Utility-Aware Legitimacy Detection of Mobile Crowdsensing Tasks via Knowledge-Based Self Organizing Feature MapIEEE Transactions on Mobile Computing10.1109/TMC.2021.313623622:6(3706-3723)Online publication date: 1-Jun-2023
  • (2022)Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile CrowdsensingICC 2022 - IEEE International Conference on Communications10.1109/ICC45855.2022.9839003(2780-2785)Online publication date: 16-May-2022

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