Cybersecurity in Transportation Systems: Policies and Technology Directions

• URL: http://arxiv.org/abs/2501.05356v1
• Date: Thu, 09 Jan 2025 16:36:40 GMT
• Title: Cybersecurity in Transportation Systems: Policies and Technology Directions
• Authors: Ostonya Thomas, M Sabbir Salek, Jean-Michel Tine, Mizanur Rahman, Trayce Hockstad, Mashrur Chowdhury,
• Abstract summary: Integrating cyberspace across transportation sectors has introduced new and deliberate cyber threats. Public agencies assumed digital infrastructure was secured since its vulnerabilities were unknown to adversaries. This study investigates some prominent reasons for the increase in cyber vulnerabilities in transportation.
• Score: 5.6728030756050964
• License:
• Abstract: The transportation industry is experiencing vast digitalization as a plethora of technologies are being implemented to improve efficiency, functionality, and safety. Although technological advancements bring many benefits to transportation, integrating cyberspace across transportation sectors has introduced new and deliberate cyber threats. In the past, public agencies assumed digital infrastructure was secured since its vulnerabilities were unknown to adversaries. However, with the expansion of cyberspace, this assumption has become invalid. With the rapid advancement of wireless technologies, transportation systems are increasingly interconnected with both transportation and non-transportation networks in an internet-of-things ecosystem, expanding cyberspace in transportation and increasing threats and vulnerabilities. This study investigates some prominent reasons for the increase in cyber vulnerabilities in transportation. In addition, this study presents various collaborative strategies among stakeholders that could help improve cybersecurity in the transportation industry. These strategies address programmatic and policy aspects and suggest avenues for technological research and development. The latter highlights opportunities for future research to enhance the cybersecurity of transportation systems and infrastructure by leveraging hybrid approaches and emerging technologies.

Related papers

• Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study [62.5909195375364]
  Cross-layer covert communication mechanism emerges as an effective strategy to mitigate regulatory challenges. We propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI) Case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication.
  arXiv  Detail & Related papers  (2025-01-19T15:05:03Z)
• Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection [0.0]
  The automotive industry increasingly adopts connected and automated vehicles (CAVs) With the emergence of new vulnerabilities and security requirements, the integration of advanced technologies presents promising avenues for enhancing CAV cybersecurity. The roadmap for cybersecurity in autonomous vehicles emphasizes the importance of efficient intrusion detection systems and AI-based techniques.
  arXiv  Detail & Related papers  (2024-12-31T04:08:42Z)
• Navigating the road to automotive cybersecurity compliance [39.79758414095764]
  The automotive industry is compelled to adopt robust cybersecurity measures to safeguard both vehicles and data against potential threats. The future of automotive cybersecurity lies in the continuous development of advanced protective measures and collaborative efforts among all stakeholders.
  arXiv  Detail & Related papers  (2024-06-29T16:07:48Z)
• Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks [55.340315838742015]
  Negligence on the security of infotainment data communication in IoV networks can unintentionally open an easy access point for social engineering attacks. In particular, we first classify data communication in the IoV network, examine the security focus of each data communication, and then develop a differentiated security architecture to provide security protection on a file-to-file basis.
  arXiv  Detail & Related papers  (2024-03-29T12:01:31Z)
• A Review on Blockchain Technologies for an Advanced and Cyber-Resilient Automotive Industry [0.0]
  This review analyzes the great potential of applying blockchain technologies to the automotive industry emphasizing its cybersecurity features. The broad adoption of blockchain unlocks a wide area of short- and medium-term promising automotive applications. Some recommendations are enumerated with the aim of guiding researchers and companies in future cyber-resilient automotive industry developments.
  arXiv  Detail & Related papers  (2024-02-01T19:23:19Z)
• Cyber-Twin: Digital Twin-boosted Autonomous Attack Detection for Vehicular Ad-Hoc Networks [8.07947129445779]
  The rapid evolution of Vehicular Ad-hoc NETworks (VANETs) has ushered in a transformative era for intelligent transportation systems (ITS) VANETs are increasingly susceptible to cyberattacks, such as jamming and distributed denial of service (DDoS) attacks. Existing methods face difficulties in detecting dynamic attacks and integrating digital twin technology and artificial intelligence (AI) models to enhance VANET cybersecurity. This study proposes a novel framework that combines digital twin technology with AI to enhance the security of RSUs in VANETs.
  arXiv  Detail & Related papers  (2024-01-25T08:05:41Z)
• Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis [71.2468615993246]
  Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. This vision paper emphasizes research challenges in deep learning-based mobility analysis that require interpretability and robustness.
  arXiv  Detail & Related papers  (2022-10-18T17:28:58Z)
• Hyperloop: A Cybersecurity Perspective [56.82349944873289]
  We provide the first analysis of the cybersecurity challenges of the interconnections between the different components of the Hyperloop ecosystem. We investigate possible infrastructure management approaches and their security concerns. We discuss countermeasures and future directions for the security of the Hyperloop design.
  arXiv  Detail & Related papers  (2022-09-07T12:10:36Z)
• Roadmap for Cybersecurity in Autonomous Vehicles [3.577310844634503]
  We discuss major automotive cyber-attacks over the past decade and present state-of-the-art solutions that leverage artificial intelligence (AI) We propose a roadmap towards building secure autonomous vehicles and highlight key open challenges that need to be addressed.
  arXiv  Detail & Related papers  (2022-01-19T16:42:18Z)
• Constraint Programming Algorithms for Route Planning Exploiting Geometrical Information [91.3755431537592]
  We present an overview of our current research activities concerning the development of new algorithms for route planning problems. The research so far has focused in particular on the Euclidean Traveling Salesperson Problem (Euclidean TSP) The aim is to exploit the results obtained also to other problems of the same category, such as the Euclidean Vehicle Problem (Euclidean VRP), in the future.
  arXiv  Detail & Related papers  (2020-09-22T00:51:45Z)
• Smart Urban Mobility: When Mobility Systems Meet Smart Data [55.456196356335745]
  Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $482.05 billion in the U.S.
  arXiv  Detail & Related papers  (2020-05-09T13:53:01Z)

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.