Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Advanced Sensing Technologies and Cybersecurity for UAV Systems
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Sensing Technologies and Cybersecurity for UAV Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1986

Special Issue Editors

Prof. Dr. Naima Kaabouch

E-Mail Website
Guest Editor
Artificial Intelligence Research (AIR) Center, University of North Dakota, Grand Forks, ND 58202, USA
Interests: artificial intelligence; cybersecurity; wireless communications; sensing; autonomous systems
Special Issues, Collections and Topics in MDPI journals
Dr. Sicong Shao

E-Mail Website
Guest Editor
School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA
Interests: cybersecurity; artificial intelligence; software engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years have seen an exponential increase in the use of Unmanned Aerial Vehicles (UAVs), including surveillance, remote sensing, and environmental monitoring, where UAV-based sensors offer accurate sensing and rapid deployment due to their mobility, light weight, and high-quality sensing capabilities. These technological advancements have established UAVs as critical mobile sensing platforms, capable of acquiring timely, cost-effective, and incredibly rich data in scenarios where traditional methods fall short. Nonetheless, the expansion of this operational field—particularly with the rise in autonomous systems—introduces significant and pressing security challenges, as UAVs become increasingly vulnerable to a wide range of cyber threats targeting drones, ground control stations, data streams, and communication links. Malicious actors can exploit the vulnerabilities to launch various attacks, such as DoS/DDoS, spoofing, and injection attacks. Hence, cybersecurity has become increasingly important for UAVs. This Special Issue aims to address challenges and present innovative methods in the field. Both original research papers and reviews are welcome. Research may focus on (but are not limited to) the following topics:

  • UAV sensors and their vulnerabilities;
  • The security of ADS-B, remote ID, and vision sensors;
  • UAV remote sensing in security operations;
  • Privacy issues related to UAV sensing techniques;
  • Cyber attacks on UAV networks (UAVs, ground control stations, and communication channels) and their impacts on airspace;
  • Cyber attack detection methods;
  • Countermeasure techniques;
  • Anomaly detection.

Prof. Dr. Naima Kaabouch
Dr. Sicong Shao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAV networks (UAV, GCS, and communication channels) cybersecurity
  • attack detection
  • communication security
  • secure sensing
  • cyber threats
  • countermeasures
  • ADS-B security
  • remote ID security
  • commend and control security
  • cryptographic techniques
  • blockchain

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

15 pages, 1273 KB  
Article
Early Detection of Spoofing Threats and Network Resilience Prediction in Drones Based on GRU and LSTM
by ChungMan Oh, JaePil Youn, WonHo Ryu and KyungShin Kim
Sensors 2026, 26(10), 3253; https://doi.org/10.3390/s26103253 - 20 May 2026
Viewed by 316
Abstract
As unmanned aerial vehicles (UAVs) are increasingly deployed in mission-critical domains such as military operations, infrastructure inspection, and disaster response, the threat of GPS and network spoofing attacks has emerged as a fundamental challenge to operational continuity. Existing intrusion detection systems based on [...] Read more.
As unmanned aerial vehicles (UAVs) are increasingly deployed in mission-critical domains such as military operations, infrastructure inspection, and disaster response, the threat of GPS and network spoofing attacks has emerged as a fundamental challenge to operational continuity. Existing intrusion detection systems based on threshold rules or shallow machine learning models are inherently limited in their ability to identify the latent onset of sophisticated, gradually intensifying spoofing campaigns—a gap that motivates the present work. This study proposes a deep learning-based early detection and network resilience prediction framework that employs Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures operating on three spatio-temporal network features—Hop Count Spike Rate (HCS), Packet Drop Volatility (PDV), and Spatial Disconnect Density (SDD)—proposed in this study. To reflect realistic adversarial conditions, we design a Gradual Adaptive Attacker model in which the spoofing intensity escalates progressively across six operational phases, including a second-stage adaptive attack that modulates its gradient upon detecting initial countermeasures. Both models are trained on 1000 simulated runs using sliding-window time-series sequences and evaluated across 500 independent test runs with paired statistical testing. The GRU model achieves a mean ROC-AUC of 0.9915 (±0.0091) and a mean F1-Score of 0.9099 (±0.0462), outperforming LSTM across all metrics with statistical significance at p < 0.001 under both the paired t-test and the Wilcoxon signed-rank test. Critically, GRU detects spoofing onset with an average latency of 0.503 time steps—approximately 29.4% faster than LSTM (0.712 steps)—a difference confirmed as statistically significant (p < 0.001, Cohen’s d = 0.41). Network resilience simulations further demonstrate that integrating GRU-based autonomous evasion maintains a Connectivity Ratio (CR) above 80% even under severe attack phases, whereas passive networks experience total connectivity collapse (CR = 0%). These findings establish GRU as the superior architecture for real-time UAV edge deployment and affirm that the proposed pipeline extends beyond threat alerting to actively preserving mission continuity under adversarial spoofing conditions. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies and Cybersecurity for UAV Systems)
►▼ Show Figures

Figure 1

Review

Jump to: Research

33 pages, 1424 KB  
Review
Security of ADS-B and Remote ID Systems: Cyberattacks, Detection Techniques, and Countermeasures
by Qinxuan Shi, Toro Dama Caleb, Sicong Shao and Naima Kaabouch
Sensors 2026, 26(2), 634; https://doi.org/10.3390/s26020634 - 17 Jan 2026
Cited by 1 | Viewed by 1235
Abstract
The aviation sector relies on cooperative surveillance systems such as Automatic Dependent Surveillance-Broadcast (ADS-B) and Remote Identification (RID) to enhance safety and efficiency. However, their open, unencrypted communication protocols make them vulnerable to various cyberattacks. This survey examines the current state of knowledge [...] Read more.
The aviation sector relies on cooperative surveillance systems such as Automatic Dependent Surveillance-Broadcast (ADS-B) and Remote Identification (RID) to enhance safety and efficiency. However, their open, unencrypted communication protocols make them vulnerable to various cyberattacks. This survey examines the current state of knowledge on attacks, detection techniques, and countermeasures for both ADS-B and RID, addressing a gap in the literature by analyzing them side by side. It categorizes attacks, including emerging threats, reviews detection methods from traditional to modern AI-based approaches and discusses existing countermeasures. Furthermore, this paper provides a list of simulation tools and open-access datasets and identifies current research challenges and future directions. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies and Cybersecurity for UAV Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop