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Adversarial Attacks on Deep Learning-based Floor Classification and Indoor Localization

Published: 28 June 2021 Publication History

Abstract

With the great advances in location-based services (LBS), Wi-Fi localization has attracted great interest due to its ubiquitous availability in indoor environments. Deep neural network (DNN) is a powerful method to achieve high localization performance using Wi-Fi signals. However, DNN models are shown vulnerable to adversarial examples generated by introducing a subtle perturbation. In this paper, we propose adversarial deep learning for indoor localization system using Wi-Fi received signal strength indicator (RSSI). In particular, we study the impact of adversarial attacks on floor classification and location prediction with Wi-Fi RSSI. Three white-box attacks methods are examined, including fast gradient sign attack (FGSM), projected gradient descent (PGD), and momentum iterative method (MIM). We validate the performance of DNN-based floor classification and location prediction using a public dataset and show that the DNN models are highly vulnerable to the three white-box adversarial attacks.

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  • (2024)Adversarial Examples Against WiFi Fingerprint-Based Localization in the Physical WorldIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.345304119(8457-8471)Online publication date: 2024
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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 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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Author Tags

  1. Adversarial Attacks
  2. Deep Learning
  3. Floor Classification
  4. Indoor Localization
  5. Received Signal Strength Indicator (RSSI)
  6. Wi-Fi

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  • Short-paper
  • Research
  • Refereed limited

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WiSec '21

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Cited By

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  • (2025)Secure Indoor Localization Against Adversarial Attacks Using DCGANIEEE Communications Letters10.1109/LCOMM.2024.350372129:1(130-134)Online publication date: Jan-2025
  • (2024)CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546771(1-6)Online publication date: 25-Mar-2024
  • (2024)Adversarial Examples Against WiFi Fingerprint-Based Localization in the Physical WorldIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.345304119(8457-8471)Online publication date: 2024
  • (2024)SENTINEL: Securing Indoor Localization Against Adversarial Attacks With Capsule Neural NetworksIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.344671743:11(4021-4032)Online publication date: Nov-2024
  • (2024)Adversarial Machine Learning for Wireless LocalizationNetwork Security Empowered by Artificial Intelligence10.1007/978-3-031-53510-9_8(213-236)Online publication date: 24-Feb-2024
  • (2023)Over-the-Air Adversarial Attacks on Deep Learning Wi-Fi FingerprintingIEEE Internet of Things Journal10.1109/JIOT.2023.323631410:11(9823-9835)Online publication date: 1-Jun-2023
  • (2023)Backdoor Attacks Against Deep Learning-Based Massive MIMO LocalizationGLOBECOM 2023 - 2023 IEEE Global Communications Conference10.1109/GLOBECOM54140.2023.10437534(2796-2801)Online publication date: 4-Dec-2023
  • (2023)BLE beacon-based floor detection for mobile robots in a multi-floor automation laboratoryTransportation Safety and Environment10.1093/tse/tdad0246:2Online publication date: 11-May-2023
  • (2022)A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof-of-ConceptInformation10.3390/info1308036313:8(363)Online publication date: 29-Jul-2022
  • (2022)Braum: Analyzing and Protecting Autonomous Machine Software Stack2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE55969.2022.00019(85-96)Online publication date: Oct-2022
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