A Comprehensive Survey of Unmanned Aerial Systems' Risks and Mitigation Strategies

• URL: http://arxiv.org/abs/2506.10327v1
• Date: Thu, 12 Jun 2025 03:30:19 GMT
• Title: A Comprehensive Survey of Unmanned Aerial Systems' Risks and Mitigation Strategies
• Authors: Sharad Shrestha, Mohammed Ababneh, Satyajayant Misra, Henry M. Cathey, Jr., Roopa Vishwanathan, Matt Jansen, Jinhong Choi, Rakesh Bobba, Yeongjin Jang,
• Abstract summary: This survey summarizes the cybersecurity vulnerabilities in several phases of UAV deployment.<n>We perform an analysis of both UAS-specific and non-UAS-specific mitigation strategies that are applicable within the UAS domain.<n>We present relevant cybersecurity standards and their recommendations in the UAS context.
• Score: 3.636441947326793
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the last decade, the rapid growth of Unmanned Aircraft Systems (UAS) and Unmanned Aircraft Vehicles (UAV) in communication, defense, and transportation has increased. The application of UAS will continue to increase rapidly. This has led researchers to examine security vulnerabilities in various facets of UAS infrastructure and UAVs, which form a part of the UAS system to reinforce these critical systems. This survey summarizes the cybersecurity vulnerabilities in several phases of UAV deployment, the likelihood of each vulnerability's occurrence, the impact of attacks, and mitigation strategies that could be applied. We go beyond the state-of-the-art by taking a comprehensive approach to enhancing UAS security by performing an analysis of both UAS-specific and non-UAS-specific mitigation strategies that are applicable within the UAS domain to define the lessons learned. We also present relevant cybersecurity standards and their recommendations in the UAS context. Despite the significant literature in UAS security and the relevance of cyberphysical and networked systems security approaches from the past, which we identify in the survey, we find several critical research gaps that require further investigation. These form part of our discussions and recommendations for the future exploration by our research community.

Related papers

• Vehicular Intrusion Detection System for Controller Area Network: A Comprehensive Survey and Evaluation [23.68939806308534]
  This paper examines existing vehicular attacks and defense strategies employed against the Controller Area Network (CAN) and specialized Electronic Control Units (ECUs)<n>The findings of our investigation reveal that the examined VIDS primarily concentrate on particular categories of attacks, neglecting the broader spectrum of potential threats.<n>We put forth several defense recommendations based on our study findings, aiming to inform and guide the future design of VIDS in the context of vehicular security.
  arXiv  Detail & Related papers  (2025-05-22T20:42:57Z)
• A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron? [30.063392019347887]
  We present a systematization of knowledge on the safety and security threats of emphComputer-Using Agents.<n> CUAs are capable of autonomously performing tasks such as navigating desktop applications, web pages, and mobile apps.
  arXiv  Detail & Related papers  (2025-05-16T06:56:42Z)
• A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network [0.0]
  Connected and Autonomous Vehicles (CAVs) enhance mobility but face cybersecurity threats.<n>Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems.<n>In-vehicle Intrusion Detection Systems (IDSs) offer a promising approach by detecting malicious activities in real time.
  arXiv  Detail & Related papers  (2025-05-15T12:38:59Z)
• LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures [49.1574468325115]
  This survey seeks to define and categorize the various attacks targeting large language models (LLMs)<n>A thorough analysis of these attacks is presented, alongside an exploration of defense mechanisms designed to mitigate such threats.
  arXiv  Detail & Related papers  (2025-05-02T10:35:26Z)
• AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement [73.0700818105842]
  We introduce AISafetyLab, a unified framework and toolkit that integrates representative attack, defense, and evaluation methodologies for AI safety.<n> AISafetyLab features an intuitive interface that enables developers to seamlessly apply various techniques.<n>We conduct empirical studies on Vicuna, analyzing different attack and defense strategies to provide valuable insights into their comparative effectiveness.
  arXiv  Detail & Related papers  (2025-02-24T02:11:52Z)
• A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments [55.60375624503877]
  Model Extraction Attacks (MEAs) threaten modern machine learning systems by enabling adversaries to steal models, exposing intellectual property and training data.<n>This survey is motivated by the urgent need to understand how the unique characteristics of cloud, edge, and federated deployments shape attack vectors and defense requirements.<n>We systematically examine the evolution of attack methodologies and defense mechanisms across these environments, demonstrating how environmental factors influence security strategies in critical sectors such as autonomous vehicles, healthcare, and financial services.
  arXiv  Detail & Related papers  (2025-02-22T03:46:50Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Sok: Comprehensive Security Overview, Challenges, and Future Directions of Voice-Controlled Systems [10.86045604075024]
  The integration of Voice Control Systems into smart devices accentuates the importance of their security. Current research has uncovered numerous vulnerabilities in VCS, presenting significant risks to user privacy and security. This study introduces a hierarchical model structure for VCS, providing a novel lens for categorizing and analyzing existing literature in a systematic manner. We classify attacks based on their technical principles and thoroughly evaluate various attributes, such as their methods, targets, vectors, and behaviors.
  arXiv  Detail & Related papers  (2024-05-27T12:18:46Z)
• Securing the Open RAN Infrastructure: Exploring Vulnerabilities in Kubernetes Deployments [60.51751612363882]
  We investigate the security implications of and software-based Open Radio Access Network (RAN) systems. We highlight the presence of potential vulnerabilities and misconfigurations in the infrastructure supporting the Near Real-Time RAN Controller (RIC) cluster.
  arXiv  Detail & Related papers  (2024-05-03T07:18:45Z)
• A Survey of Security in UAVs and FANETs: Issues, Threats, Analysis of Attacks, and Solutions [1.0923877073891446]
  It is critical that security is ensured for UAVs and the networks that provide communication between UAVs. This survey seeks to provide a comprehensive perspective on security within the domain of UAVs and Flying Ad Hoc Networks (FANETs)
  arXiv  Detail & Related papers  (2023-06-25T16:15:40Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.