Addressing security vulnerabilities in autonomous vehicles through resilient frameworks and robust cyber defense systems

The rapid advancement of autonomous vehicle (AV) technology has introduced significant security vulnerabilities that pose risks to passenger safety, system integrity, and public trust. This paper investigates the critical security challenges facing AVs, including sensor spoofing, GPS jamming, adversarial machine learning attacks, and communication network breaches. Through an extensive review of recent cyber threats, the study highlights the increasing sophistication of cyber-attacks targeting AV ecosystems and the potential consequences of security breaches. To address these vulnerabilities, the paper explores the implementation of resilient frameworks and robust cyber defense systems. Specifically, it examines the role of artificial intelligence-driven anomaly detection, blockchain-based secure communication, encryption techniques, and intrusion detection and prevention systems in mitigating security risks. Additionally, it evaluates the effectiveness of cybersecurity-by-design approaches, emphasizing proactive security measures in AV development.Key findings indicate that a multi-layered security approach integrating real-time threat detection, automated response mechanisms, and continuous system monitoring is essential for enhancing AV resilience. Furthermore, collaboration between industry stakeholders, policymakers, and cybersecurity experts is crucial in developing standardized security protocols and regulatory frameworks. The study underscores the necessity of adopting an adaptive and resilient security architecture to safeguard AVs against evolving cyber threats. By implementing robust cyber defense mechanisms and fostering a security-conscious AV ecosystem, the risks associated with autonomous vehicle operations can be significantly reduced, ensuring safer and more reliable transportation systems. Keywords: Autonomous Vehicles, Cybersecurity, Security Vulnerabilities, Resilient Frameworks, Cyber Defense Mechanisms, Machine Learning Security, AI-driven Automation, Vehicle-to-Vehicle Communication.

Authors

Institutions

Publication Details

Journal
Gulf Journal of Advance Business Research
Published
2025-03-12
DOI
https://doi.org/10.51594/gjabr.v3i3.114
Citations
2
Primary Topic
Safety Systems Engineering in Autonomy
Type
article
Field-Weighted Citation Impact
2.61
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Addressing security vulnerabilities in autonomous vehicles through resilient frameworks and robust cyber defense systems

Abiodun Sunday Adebayo, Olanrewaju Oluwaseun Ajayi, Naomi Chukwurah
2 citations
Gulf Journal of Advance Business Research
Safety Systems Engineering in Autonomy
2.61
article

Addressing security vulnerabilities in autonomous vehicles through resilient frameworks and robust cyber defense systems

Abiodun Sunday Adebayo, Olanrewaju Oluwaseun Ajayi, Naomi Chukwurah
article en
2 citations

Abstract

The rapid advancement of autonomous vehicle (AV) technology has introduced significant security vulnerabilities that pose risks to passenger safety, system integrity, and public trust. This paper investigates the critical security challenges facing AVs, including sensor spoofing, GPS jamming, adversarial machine learning attacks, and communication network breaches. Through an extensive review of recent cyber threats, the study highlights the increasing sophistication of cyber-attacks targeting AV ecosystems and the potential consequences of security breaches. To address these vulnerabilities, the paper explores the implementation of resilient frameworks and robust cyber defense systems. Specifically, it examines the role of artificial intelligence-driven anomaly detection, blockchain-based secure communication, encryption techniques, and intrusion detection and prevention systems in mitigating security risks. Additionally, it evaluates the effectiveness of cybersecurity-by-design approaches, emphasizing proactive security measures in AV development.Key findings indicate that a multi-layered security approach integrating real-time threat detection, automated response mechanisms, and continuous system monitoring is essential for enhancing AV resilience. Furthermore, collaboration between industry stakeholders, policymakers, and cybersecurity experts is crucial in developing standardized security protocols and regulatory frameworks. The study underscores the necessity of adopting an adaptive and resilient security architecture to safeguard AVs against evolving cyber threats. By implementing robust cyber defense mechanisms and fostering a security-conscious AV ecosystem, the risks associated with autonomous vehicle operations can be significantly reduced, ensuring safer and more reliable transportation systems. Keywords: Autonomous Vehicles, Cybersecurity, Security Vulnerabilities, Resilient Frameworks, Cyber Defense Mechanisms, Machine Learning Security, AI-driven Automation, Vehicle-to-Vehicle Communication.

Gulf Journal of Advance Business ResearchVol. 3(3)
University of Staffordshire (GB), University of the Cumberlands (US), Film Independent (US)
Openalex Percentile: Top 13%
Safety Systems Engineering in Autonomy
2.61
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.