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Drones
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AI for Cybersecurity in Unmanned Aerial Systems (UAS)
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AI for Cybersecurity in Unmanned Aerial Systems (UAS)

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 1005

Special Issue Editors

Dr. Hassan El Alami

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science (EECS), Howard University, Washington, DC 20059, USA
Interests: Artificial Intelligence; cybersecurity; autonomous systems; IoT; metaverse
Special Issues, Collections and Topics in MDPI journals
Dr. Neji Mensi

E-Mail Website
Guest Editor
College of Engineering and Science, Electrical Engineering and Computer Science, Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901, USA
Interests: cybersecurity; reconfigurable intelligent surfaces (RIS); wireless communications; UAV; metaverse; IoT/CPS
Dr. Fatima Salahdine

E-Mail Website
Guest Editor
Cybersecurity Workforce Certification Training, Purdue University Northwest University, Hammond, ID 46324, USA
Interests: UAV; electrical engineering; wireless communication; machine learning; IoT

Special Issue Information

Dear Colleagues,

We invite submissions for a Special Issue of Drones on "AI for Cybersecurity in Unmanned Aerial Systems (UASs)", focusing on the use of artificial intelligence (AI) to enhance security, resilience, and operational safety in UASs.

Scope of the Special Issue: Unmanned aerial systems (UASs) are increasingly being utilized across various industries, including defense, agriculture, logistics, and emergency responses. As their deployment expands, ensuring the cybersecurity of UASs becomes critically important. AI techniques, such as transformers, large language models (LLMs), machine learning, and deep learning, offer promising solutions to enhance cybersecurity. These technologies enable advanced threat detection, improve system resilience, and provide intelligent defenses against potential threats, making UASs more secure and robust.

This Special Issue seeks innovative research and insights into how AI technologies, such as transformers, can be applied to improve the cybersecurity of UASs. Submissions that explore AI-driven solutions for threat detection, secure communication, and the overall resilience of UASs are welcome. We also encourage papers that investigate the integration of AI models with real-world drone simulators to test and validate security measures in realistic environments. Contributions that include the creation or use of datasets for cybersecurity research in the UAS context are also encouraged, as they provide a foundation for developing AI-based models and validating their effectiveness. Topics of interest include, but are not limited to, the following:

  • AI-based cybersecurity frameworks for UASs.
  • Transformers and other advanced AI models for securing UASs.
  • Machine learning and deep learning models for UAS cybersecurity.
  • AI-driven secure communication protocols for UAS networks.
  • Real-time monitoring and anomaly detection in UASs using AI.
  • AI-enhanced resilience strategies for UASs.
  • Autonomous systems with built-in AI security features.
  • AI for ensuring data privacy and integrity in UAS operations.
  • AI-enabled security for remote UAS control and command systems.
  • AI in UAS risk assessment and vulnerability mitigation.
  • Intelligent decision-making systems for UAS security.
  • Datasets for cybersecurity in the context of UASs.
  • Integration of AI models with real drone simulators for cybersecurity testing.

We welcome both theoretical research and practical case studies that highlight the application of AI to bolster cybersecurity in UASs. Papers introducing new datasets, exploring the role of transformers in cybersecurity, or integrating AI models with real-world drone simulators will be of particular interest.

Dr. Hassan El Alami
Dr. Neji Mensi
Dr. Fatima Salahdine
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Drones is an international peer-reviewed open access monthly 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

  • AI-based cybersecurity frameworks
  • UAS cybersecurity
  • advanced AI models
  • secure communication protocols
  • autonomous systems
  • data privacy

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Published Papers (2 papers)

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31 pages, 4033 KiB  
Article
XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
by Qian Sun, Jie Zeng, Lulu Dai, Yangliu Hu and Lin Tian
Drones 2025, 9(5), 324; https://doi.org/10.3390/drones9050324 - 23 Apr 2025
Viewed by 340
Abstract
Although deep learning (DL) methods are effective for detecting protocol attacks involving drones in sixth-generation (6G) nonterrestrial networks (NTNs), classifying novel attacks and identifying anomalous sequences remain challenging. The internal capture processes and matching results of DL models are useful for addressing these [...] Read more.
Although deep learning (DL) methods are effective for detecting protocol attacks involving drones in sixth-generation (6G) nonterrestrial networks (NTNs), classifying novel attacks and identifying anomalous sequences remain challenging. The internal capture processes and matching results of DL models are useful for addressing these issues. The key challenges involve obtaining this internal information from DL-based anomaly detection methods, using this internal information to establish new classifications for uncovered protocol attacks and tracing the input back to the anomalous protocol sequences. Therefore, in this paper, we propose an interpretable anomaly classification and identification method for 6G NTN protocols. We design an interpretable anomaly detection framework for 6G NTN protocols. In particular, we introduce explainable artificial intelligence (XAI) techniques to obtain internal information, including the matching results and capture process, and design a collaborative approach involving different detection methods to utilize this internal information. We also design a self-evolving classification method for the proposed interpretable framework to classify uncovered protocol attacks. The rule and baseline detection approaches are made transparent and work synergistically to extract and learn from the fingerprint features of the uncovered protocol attacks. Furthermore, we propose an online method to identify anomalous protocol sequences; this intrinsic interpretable identification approach is based on a two-layer deep neural network (DNN) model. The simulation results show that the proposed classification and identification methods can be effectively used to classify uncovered protocol attacks and identify anomalous protocol sequences, with the precision increasing by a maximum of 32.8% and at least 26%, respectively, compared with that of existing methods. Full article
(This article belongs to the Special Issue AI for Cybersecurity in Unmanned Aerial Systems (UAS))
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Review

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57 pages, 24887 KiB  
Review
AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
by Zheng Yang, Yuting Zhang, Jie Zeng, Yifan Yang, Yufei Jia, Hua Song, Tiejun Lv, Qian Sun and Jianping An
Drones 2025, 9(6), 392; https://doi.org/10.3390/drones9060392 - 23 May 2025
Abstract
As unmanned aerial vehicle (UAV) applications expand across logistics, agriculture, and emergency response, safety and security threats are becoming increasingly complex. Addressing these evolving threats, including physical safety and network security threats, requires continued advancement by integrating traditional artificial intelligence (AI) tools such [...] Read more.
As unmanned aerial vehicle (UAV) applications expand across logistics, agriculture, and emergency response, safety and security threats are becoming increasingly complex. Addressing these evolving threats, including physical safety and network security threats, requires continued advancement by integrating traditional artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL), which contribute to significantly enhancing UAV safety and security. Large language models (LLMs), a cutting-edge trend in the AI field, are associated with strong capabilities for learning and adapting across various environments. Their emergence reflects a broader trend toward intelligent systems that may eventually demonstrate behavior comparable to human-level reasoning. This paper summarizes the typical safety and security threats affecting UAVs, reviews the progress of traditional AI technologies, as described in the literature, and identifies strategies for reducing the impact of such threats. It also highlights the limitations of traditional AI technologies and summarizes the current application status of LLMs in UAV safety and security. Finally, this paper discusses the challenges and future research directions for improving UAV safety and security with LLMs. By leveraging their advanced capabilities, LLMs offer potential benefits in critical domains such as urban air traffic management, precision agriculture, and emergency response, fostering transformative progress toward adaptive, reliable, and secure UAV systems that address modern operational complexities. Full article
(This article belongs to the Special Issue AI for Cybersecurity in Unmanned Aerial Systems (UAS))
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