Detection Performance of Malicious UAV using Massive IoT Networks

Suhail Al-Dharrab*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


This paper investigates the fundamental performance limits in detecting malicious drones and mini-unmanned aerial vehicles (UAVs) using massive RF-based sensors under multipath fading channels. Although drones and/or small UAVs have many civilian and military applications, their prevalence raised security concerns if they have been controlled to breach into restricted areas. In this work, the RF-based sensing of unauthorized drones is adopted with well-distributed sensors in an urban environment. Detection performance using Neyman-Pearson criterion with Bayesian inference is analyzed and closed-form expressions for the probability of detection are derived. The derived expressions are corroborated with extensive Monte-Carlo simulations to demonstrate the severe effect of environmental conditions, e.g. suburban/dense, observation dimensions of the sufficient statistics, and sensor locations on the detection performance.

Original languageEnglish
Title of host publication2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350311143
StatePublished - 2023
Event97th IEEE Vehicular Technology Conference, VTC 2023-Spring - Florence, Italy
Duration: 20 Jun 202323 Jun 2023

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference97th IEEE Vehicular Technology Conference, VTC 2023-Spring

Bibliographical note

Publisher Copyright:
© 2023 IEEE.


  • Drone detection
  • Neyman-Pearson lemma
  • massive IoT networks
  • sufficient statistics
  • unmanned aerial vehicles (UAVs)

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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