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Stealth Spectrum Sensing Data Falsification Attacks Affecting IoT Spectrum Monitors on the Battlefield


Sánchez Sánchez, Pedro Miguel; Tomás Martínez Beltrán, Enrique; Celdran, Alberto Huertas; Wassink, Robin; Bovet, Gérôme; Pérez, Gregorio Martínez; Stiller, Burkhard (2023). Stealth Spectrum Sensing Data Falsification Attacks Affecting IoT Spectrum Monitors on the Battlefield. In: MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM), Boston, MA, USA, 30 October 2023 - 3 November 2023. Institut of Electrical and Electronics Engineers, 673-678.

Abstract

Resource-constrained spectrum sensors from the Internet of Battlefield Things (IoBT) monitor the frequency spectrum to communicate over unoccupied bands, intercept enemy transmissions, or decode valuable information. However, they are also susceptible to Spectrum Sensing Data Falsification (SSDF) attacks manipulating the sensing data and impacting the previous services. Detection systems based on fingerprinting and machine learning have shown promising performance while detecting existing SSDF attacks. However, novel attacks reducing their impact on sensors behaviors have not been analyzed yet. Thus, this work redesigns and reimplements seven SSDF attacks by modifying spectrum data in the sensor memory instead of at later stages in the file system. Several experiments with current intelligent detection systems demonstrated that more effort is needed from the defensive perspective since the new SSDF attacks evade their detection. In this sense, literature-based detection methods achieve less than a 0.50 True Positive Rate when detecting the new implementations of the attacks.

Abstract

Resource-constrained spectrum sensors from the Internet of Battlefield Things (IoBT) monitor the frequency spectrum to communicate over unoccupied bands, intercept enemy transmissions, or decode valuable information. However, they are also susceptible to Spectrum Sensing Data Falsification (SSDF) attacks manipulating the sensing data and impacting the previous services. Detection systems based on fingerprinting and machine learning have shown promising performance while detecting existing SSDF attacks. However, novel attacks reducing their impact on sensors behaviors have not been analyzed yet. Thus, this work redesigns and reimplements seven SSDF attacks by modifying spectrum data in the sensor memory instead of at later stages in the file system. Several experiments with current intelligent detection systems demonstrated that more effort is needed from the defensive perspective since the new SSDF attacks evade their detection. In this sense, literature-based detection methods achieve less than a 0.50 True Positive Rate when detecting the new implementations of the attacks.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Artificial Intelligence
Physical Sciences > Computer Networks and Communications
Physical Sciences > Signal Processing
Physical Sciences > Safety, Risk, Reliability and Quality
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:3 November 2023
Deposited On:09 Feb 2024 08:22
Last Modified:06 Mar 2024 14:41
Publisher:Institut of Electrical and Electronics Engineers
ISBN:979-8-3503-2181-4
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/milcom58377.2023.10356249
Other Identification Number:merlin-id:24398