Journal of Engineering in Industrial Research

h-index: 18     i10-index: 25

Cybersecurityin Autonomous Systems: Threats, ‎Vulnerabilities, and Defense Mechanisms

Document Type : Original Research Article

Author

Seyed Milad Kashefi Pour Dezfuli

Department of Law, Florida International University, Florida, U.S.A

https://doi.org/10.48309/jeires.2025.512281.1180
Abstract
There is the rapid evolution of autonomous systems, including autonomous vehicles, drones, and smart infrastructure, that has revolutionized industries through enhanced efficiency, precision, and scalability. It has, however, been accompanied by significant cybersecurity risks. Autonomous systems are susceptible to all types of cyber-attacks because of their connectivity to networks, including data tampering, sensor spoofing, denial-of-service attacks, and unauthorized access to control systems. These vulnerabilities stem from the integration of complex software architectures, communications networks, and sensor suites that offer a number of attack surfaces to malicious agents. This study provides a comprehensive assessment of the primary cybersecurity threats and vulnerabilities in autonomous systems, with an emphasis on their ability to affect safety, privacy, and operational integrity. Moreover, it analyzes state-of-the-art protection methods, including intrusion detection systems, encryption protocols, anomaly-based monitoring, and machine learning-based threat prevention methods. By presenting real case studies and current research advances, this study advocates for the urgent need for secure, adaptive, and multi-level security architectures for safeguarding autonomous systems against new and rising cyber threats. The findings stress the importance of coordination among cybersecurity researchers, system engineers, and policy makers to ensure the secure and dependable introduction of autonomous technologies into mission-critical applications.

Keywords

Autonomous systems
‎Cybersecurity
Threats
‎Vulnerabilities
Defense mechanisms
‎Intrusion detection.‎

Subjects

OPEN ACCESS

©2025 The author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

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Journal of Engineering in Industrial Research
Volume 6, Issue 2
Winter 2025
Pages 179-194

  • Receive Date 13 March 2025
  • Revise Date 23 March 2025
  • Accept Date 14 April 2025

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