Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs
- URL: http://arxiv.org/abs/2312.14687v1
- Date: Fri, 22 Dec 2023 13:42:53 GMT
- Title: Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs
- Authors: Ioannis Mavromatis, Theodoros Spyridopoulos, Pietro Carnelli, Woon Hau Chin, Ahmed Khalil, Jennifer Chakravarty, Lucia Cipolina Kun, Robert J. Piechocki, Colin Robbins, Daniel Cunnington, Leigh Chase, Lamogha Chiazor, Chris Preston, Rahul, Aftab Khan,
- Abstract summary: Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution.
This paper presents an envisaged Cybersecurity Centre of Excellence (CSCE) designed to bolster research, testing, and evaluation of the cybersecurity of C-ITSs.
- Score: 11.853500347907826
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The way we travel is changing rapidly, and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for ensuring safety and reliability. Building on this premise, this paper presents an envisaged Cybersecurity Centre of Excellence (CSCE) designed to bolster research, testing, and evaluation of the cybersecurity of C-ITSs. We explore the design, functionality, and challenges of CSCE's testing facilities, outlining the technological, security, and societal requirements. Through a thorough survey and analysis, we assess the effectiveness of these systems in detecting and mitigating potential threats, highlighting their flexibility to adapt to future C-ITSs. Finally, we identify current unresolved challenges in various C-ITS domains, with the aim of motivating further research into the cybersecurity of C-ITSs.
Related papers
- SoK: Identifying Limitations and Bridging Gaps of Cybersecurity Capability Maturity Models (CCMMs) [1.2016264781280588]
Cybersecurity Capability Maturity Models ( CCMMs) emerge as pivotal tools in enhancing organisational cybersecurity posture.
CCMMs provide a structured framework to guide organisations in assessing their current cybersecurity capabilities, identifying critical gaps, and prioritising improvements.
However, the full potential of CCMMs is often not realised due to inherent limitations within the models and challenges encountered during their implementation and adoption processes.
arXiv Detail & Related papers (2024-08-28T21:00:20Z) - EAIRiskBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [47.69642609574771]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.
Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.
However, the deployment of these agents in physical environments presents significant safety challenges.
This study introduces EAIRiskBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z) - Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI [0.0]
A key area in AI-based cybersecurity focuses on defending deep neural networks against malicious perturbations.
We attempt to validate results from prior work on certified robustness using the VeriGauge toolkit.
Our findings underscore the urgent need for standardized methodologies, containerization, and comprehensive documentation.
arXiv Detail & Related papers (2024-05-29T04:37:19Z) - 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) - Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI.
The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees.
We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
arXiv Detail & Related papers (2024-05-10T17:38:32Z) - Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content.
In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks.
We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
arXiv Detail & Related papers (2024-02-21T06:22:41Z) - The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge.
This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI)
We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
arXiv Detail & Related papers (2024-01-03T07:47:22Z) - Trust-based Approaches Towards Enhancing IoT Security: A Systematic Literature Review [3.0969632359049473]
This research paper presents a systematic literature review on the Trust-based cybersecurity security approaches for IoT.
We highlighted the common trust-based mitigation techniques in existence for dealing with these threats.
Several open issues were highlighted, and future research directions presented.
arXiv Detail & Related papers (2023-11-20T12:21:35Z) - Leveraging Traceability to Integrate Safety Analysis Artifacts into the
Software Development Process [51.42800587382228]
Safety assurance cases (SACs) can be challenging to maintain during system evolution.
We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models.
We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
arXiv Detail & Related papers (2023-07-14T16:03:27Z) - Towards Safer Generative Language Models: A Survey on Safety Risks,
Evaluations, and Improvements [76.80453043969209]
This survey presents a framework for safety research pertaining to large models.
We begin by introducing safety issues of wide concern, then delve into safety evaluation methods for large models.
We explore the strategies for enhancing large model safety from training to deployment.
arXiv Detail & Related papers (2023-02-18T09:32:55Z) - A Comprehensive Survey on the Cyber-Security of Smart Grids:
Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions [0.5735035463793008]
We provide a classification of attacks based on the Open System Interconnection (OSI) model.
We discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication.
arXiv Detail & Related papers (2022-06-22T14:55:06Z)
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.