End-to-End Co-Simulation Testbed for Cybersecurity Research and Development in Intelligent Transportation Systems

• URL: http://arxiv.org/abs/2509.16489v1
• Date: Sat, 20 Sep 2025 01:21:54 GMT
• Title: End-to-End Co-Simulation Testbed for Cybersecurity Research and Development in Intelligent Transportation Systems
• Authors: Minhaj Uddin Ahmad, Akid Abrar, Sagar Dasgupta, Mizanur Rahman,
• Abstract summary: This chapter discusses an integrated co-simulation testbed that links CARLA for 3D environment and sensor modeling, SUMO for microscopic traffic simulation and control, and OMNeT++ for V2X communication simulation.<n>The co-simulation testbed enables end-to-end experimentation, vulnerability identification, and mitigation benchmarking.<n>To illustrate its capabilities, the chapter incorporates a case study on a C-V2X proactive safety alert system enhanced with post-quantum cryptography.
• Score: 5.804791448287085
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Intelligent Transportation Systems (ITS) have been widely deployed across major metropolitan regions worldwide to improve roadway safety, optimize traffic flow, and reduce environmental impacts. These systems integrate advanced sensors, communication networks, and data analytics to enable real-time traffic monitoring, adaptive signal control, and predictive maintenance. However, such integration significantly broadens the ITS attack surface, exposing critical infrastructures to cyber threats that jeopardize safety, data integrity, and operational resilience. Ensuring robust cybersecurity is therefore essential, yet comprehensive vulnerability assessments, threat modeling, and mitigation validations are often cost-prohibitive and time-intensive when applied to large-scale, heterogeneous transportation systems. Simulation platforms offer a cost-effective and repeatable means for cybersecurity evaluation, and the simulation platform should encompass the full range of ITS dimensions - mobility, sensing, networking, and applications. This chapter discusses an integrated co-simulation testbed that links CARLA for 3D environment and sensor modeling, SUMO for microscopic traffic simulation and control, and OMNeT++ for V2X communication simulation. The co-simulation testbed enables end-to-end experimentation, vulnerability identification, and mitigation benchmarking, providing practical insights for developing secure, efficient, and resilient ITS infrastructures. To illustrate its capabilities, the chapter incorporates a case study on a C-V2X proactive safety alert system enhanced with post-quantum cryptography, highlighting the role of the testbed in advancing secure and resilient ITS infrastructures.

Related papers

• A Survey of Security Challenges and Solutions for UAS Traffic Management (UTM) and small Unmanned Aerial Systems (sUAS) [25.67972631925627]
  Small Unmanned Aerial Systems (sUAS) for civil and commercial missions are vulnerable to cyber-security threats.<n>This paper presents a comprehensive survey of cyber-security vulnerabilities and defenses tailored to the sUAS and UTM ecosystem.
  arXiv  Detail & Related papers  (2026-01-13T05:18:49Z)
• Security-Aware Joint Sensing, Communication, and Computing Optimization in Low Altitude Wireless Networks [83.84711311344918]
  Integrated sensing, communications, and computing (I SCC) is one of the core parts of Low-Altitude Wireless Networks (LAWNs)<n>This paper studies joint performance optimization of I SCC while considering secrecyness of the communications.<n>We propose a deep Q-network (DQN)-based multi-objective evolutionary algorithm, which adaptively selects evolutionary operators according to the evolving optimization objectives.
  arXiv  Detail & Related papers  (2025-11-03T11:06:41Z)
• OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows [77.95511352806261]
  Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.<n>We propose OS-Sentinel, a novel hybrid safety detection framework that combines a Formal Verifier for detecting explicit system-level violations with a Contextual Judge for assessing contextual risks and agent actions.
  arXiv  Detail & Related papers  (2025-10-28T13:22:39Z)
• Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models [5.757331432762268]
  This study presents a novel multi-agent framework to enhance traffic simulation and cybersecurity testing.<n>The framework automates the creation of traffic scenarios, the design of cyberattack strategies, and the development of defense mechanisms.<n>Results show a 10.2 percent increase in travel time during an attack, which is reduced by 3.3 percent with the defense strategy.
  arXiv  Detail & Related papers  (2025-06-20T02:41:23Z)
• A Large Language Model-Supported Threat Modeling Framework for Transportation Cyber-Physical Systems [11.872361272221244]
  TraCR-TMF identifies threats, potential attack techniques, and relevant countermeasures for transportation CPS.<n>TraCR-TMF offers LLM-based attack path identification for critical assets based on vulnerabilities across transportation CPS entities.<n>It incorporates the Common Vulnerability Scoring System (CVSS) scores of known exploited vulnerabilities to prioritize threat mitigations.
  arXiv  Detail & Related papers  (2025-06-01T04:33:34Z)
• SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator [77.86600052899156]
  Large Language Model (LLM)-based agents are increasingly deployed in real-world applications.<n>We propose AutoSafe, the first framework that systematically enhances agent safety through fully automated synthetic data generation.<n>We show that AutoSafe boosts safety scores by 45% on average and achieves a 28.91% improvement on real-world tasks.
  arXiv  Detail & Related papers  (2025-05-23T10:56:06Z)
• A Systematic Review of Security Vulnerabilities in Smart Home Devices and Mitigation Techniques [0.0]
  The study explores security threats in smart homes ecosystems, categorizing them into vulnerabilities at the network layer, device level, and those from cloud-based and AI-driven systems.<n>Research findings indicate that post-quantum encryption, coupled with AI-driven anomaly detection, is highly effective in enhancing security.
  arXiv  Detail & Related papers  (2025-04-03T00:03:53Z)
• SafeCast: Risk-Responsive Motion Forecasting for Autonomous Vehicles [12.607007386467329]
  We present SafeCast, a risk-responsive motion forecasting model.<n>It integrates safety-aware decision-making with uncertainty-aware adaptability.<n>Our model achieves state-of-the-art (SOTA) accuracy while maintaining a lightweight architecture and low inference latency.
  arXiv  Detail & Related papers  (2025-03-28T15:38:21Z)
• Simulation of Multi-Stage Attack and Defense Mechanisms in Smart Grids [2.0766068042442174]
  We introduce a simulation environment that replicates the power grid's infrastructure and communication dynamics.<n>The framework generates diverse, realistic attack data to train machine learning algorithms for detecting and mitigating cyber threats.<n>It also provides a controlled, flexible platform to evaluate emerging security technologies, including advanced decision support systems.
  arXiv  Detail & Related papers  (2024-12-09T07:07:17Z)
• VMGuard: Reputation-Based Incentive Mechanism for Poisoning Attack Detection in Vehicular Metaverse [52.57251742991769]
  vehicular Metaverse guard (VMGuard) protects vehicular Metaverse systems from data poisoning attacks.<n>VMGuard implements a reputation-based incentive mechanism to assess the trustworthiness of participating SIoT devices.<n>Our system ensures that reliable SIoT devices, previously missclassified, are not barred from participating in future rounds of the market.
  arXiv  Detail & Related papers  (2024-12-05T17:08:20Z)
• ACRIC: Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
  Recent security incidents in safety-critical industries exposed how the lack of proper message authentication enables attackers to inject malicious commands or alter system behavior.<n>These shortcomings have prompted new regulations that emphasize the pressing need to strengthen cybersecurity.<n>We introduce ACRIC, a message authentication solution to secure legacy industrial communications.
  arXiv  Detail & Related papers  (2024-11-21T18:26:05Z)
• 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)
• SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic [49.549771439609046]
  We propose SISSA, a SOME/IP communication traffic-based approach for modeling and analyzing in-vehicle functional safety and cyber security. Specifically, SISSA models hardware failures with the Weibull distribution and addresses five potential attacks on SOME/IP communication. Extensive experimental results show the effectiveness and efficiency of SISSA.
  arXiv  Detail & Related papers  (2024-02-21T03:31:40Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• SCART: Simulation of Cyber Attacks for Real-Time [0.1633272850273525]
  This paper introduces a novel cyber-attack simulation infrastructure designed to enhance simulation environments for real-time systems.<n>We present the SCART framework and dataset, addressing a central challenge in real-time systems: the lack of scalable testing environments.<n>By leveraging simulation-based capabilities, the framework generates training and testing data for data-driven approaches, such as machine learning.
  arXiv  Detail & Related papers  (2023-04-07T14:25:30Z)

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.