Ahmed Abdo

ORCID: 0009-0004-2940-3695
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About
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Research Areas
  • Vehicular Ad Hoc Networks (VANETs)
  • Blockchain Technology Applications and Security
  • Traffic control and management
  • Autonomous Vehicle Technology and Safety
  • User Authentication and Security Systems
  • Forensic Toxicology and Drug Analysis
  • Adversarial Robustness in Machine Learning
  • Cryptography and Data Security
  • Advanced Malware Detection Techniques
  • Advanced Authentication Protocols Security
  • Information and Cyber Security
  • Network Security and Intrusion Detection

University of California, Riverside
2019-2024

Connected and automated vehicles (CAVs) provide various valuable advanced services to manufacturers, owners, mobility service providers, transportation authorities. As a result, large number of CAV applications have been proposed improve the safety, mobility, sustainability system. With increasing connectivity automation, cybersecurity connected system (CATS) has raised attention community in recent years. Vulnerabilities CAVs can lead breakdowns compromise safety (e.g., causing crashes),...

10.1109/tiv.2023.3326736 article EN IEEE Transactions on Intelligent Vehicles 2023-10-23

While connected vehicle (CV) applications have the potential to revolutionize traditional transportation system, cyber and physical attacks on them may lead disastrous consequences. In this work, we propose an efficient dual cyber-physical blockchain framework build trust secure communication for CV applications. Our approach incorporates technology sensing capabilities of vehicles quickly react in a large-scale vehicular network, with low resource overhead. We explore application our three...

10.1109/iv48863.2021.9575869 article EN 2022 IEEE Intelligent Vehicles Symposium (IV) 2021-07-11

Connected and automated vehicle (CAV) technology has the potential to greatly improve transportation mobility, safety, energy efficiency. However, ubiquitous vehicular connectivity also opens up door cyberattacks. In this study, we investigate cybersecurity risks of a representative cooperative traffic management application, i.e., highway on-ramp merging, in mixed environment. We develop threat models with two trajectory spoofing strategies on CAVs create congestion devise an...

10.1109/mits.2022.3151097 article EN IEEE Intelligent Transportation Systems Magazine 2022-04-05

Connected vehicles nowadays can provide a variety of useful and advanced services to their owners, manufacturers, transportation authorities, other mobility service providers. Securing the complex sensing networking protocols that enable these applications is an important difficult problem. In this paper, we use blockchain which traditionally used in from cryptocurrencies smart contracts, as potential solution CV security. Specifically, exploit immutability ensure safety falsified...

10.1109/iv48863.2021.9575411 article EN 2022 IEEE Intelligent Vehicles Symposium (IV) 2021-07-11

Connected vehicle (CV) applications promise to revolutionize our transportation systems, improving safety and traffic capacity while reducing environmental footprint. Many CV have been proposed towards these goals, with the US Department of Transportation (USDOT) recently initiating some designated deployment sites enable experimentation validation. While focus this initial development effort is on demonstrating functionality a range applications, recent attacks demonstrated their...

10.1109/iv51971.2022.9827191 article EN 2022 IEEE Intelligent Vehicles Symposium (IV) 2022-06-05

While connected vehicle (CV) applications have the potential to revolutionize traditional transportation system, cyber and physical attacks on them could be devastating. In this work, we propose an efficient dual cyber-physical blockchain framework build trust secure communication for CV applications. Our approach incorporates technology sensing capabilities of vehicles quickly react in a large-scale vehicular network, with low resource overhead. We explore application our three...

10.48550/arxiv.2102.07690 preprint EN cc-by-nc-nd arXiv (Cornell University) 2021-01-01
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