Misbehaviour detection and trustworthy collaboration in vehicular communication networks
(English) The integration of advanced wireless technologies, e.g., cellular and IEEE 802.11p, in modern vehicles enables vehicle-to-everything (V2X) communication, fostering the next-generation Internet-of-Vehicles (IoV). The rise of IoV leads to more connected vehicles on roads, capable of making i...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/692552 |
| Acceso en línea: | http://hdl.handle.net/10803/692552 https://dx.doi.org/10.5821/dissertation-2117-418119 |
| Access Level: | acceso abierto |
| Palabra clave: | Vehicle-to-everything (V2X) Communication Internet-of-Vehicles (IoV) Artificial Intelligence (AI) Machine Learning (ML) Reinforcement Learning (RL) Deep Reinforcement Learning (DRL) Transfer Learning (TL) Vehicular Communication V2X Cybersecurity Closed-Loop Security Misbehaviour Detection Adversarial Defence Trust Management Collaborative Misbehaviour Detection Vehicle Authentication Comunicació vehicle a tot (V2X) Internet dels vehicles (IoV) Intel ·ligència artificial (IA) Aprenentatge automàtic (ML) Aprenentatge per reforç (RL) Aprenentatge per reforç profund (DRL) Aprenentatge per transferència (TL) Comunicació vehicular Ciberseguretat V2X Seguretat de circuit tancat Detecció de males conductes Defensa adversarial Gestió de confiança Detecció col·laborativa de males conductes Autenticació de vehicles Comunicación vehículo a todo (V2X) Internet de los vehículos (IoV) Inteligencia artificial (IA) Aprendizaje automático (ML) Aprendizaje por refuerzo (RL) Aprendizaje por refuerzo profundo (DRL) Aprendizaje por transferencia (TL) Comunicación vehicular Ciberseguridad V2X Seguridad de circuito cerrado Detección de malas conductas Gestión de confianza Detección colaborativa de malas conductas Autenticación de vehículos Àrees temàtiques de la UPC::Enginyeria de la telecomunicació 621.3 |
| Sumario: | (English) The integration of advanced wireless technologies, e.g., cellular and IEEE 802.11p, in modern vehicles enables vehicle-to-everything (V2X) communication, fostering the next-generation Internet-of-Vehicles (IoV). The rise of IoV leads to more connected vehicles on roads, capable of making informed and coordinated decisions through real-time information sharing among vehicles, communication infrastructure, pedestrians, or roadside units (RSUs). However, V2X and IoV technologies inadvertently bring unprecedented challenges involving security and privacy vulnerabilities. Security threats and attacks can emerge from both malicious outsiders and insiders in V2X communication. Detecting and containing misbehaviours, particularly those initiated by rogue insiders, present challenging yet critical tasks for ensuring road safety. Furthermore, the pervasive use of artificial intelligence and machine learning (AI/ML) tools across various aspects poses potential threats to secure V2X operations. Motivated by these challenges, this doctoral thesis focuses on enhancing the security, robustness, and trustworthiness of V2X communications by enabling efficient and effective misbehaviour detection and fostering trustworthy collaboration. Specifically, we focus on (i) achieving effective and efficient misbehaviour detection with high accuracy and minimal false alarms, leveraging diverse spatiotemporal characteristics in vehicular data, and (ii) facilitating trustworthy information sharing for collaborative misbehaviour detection, with an emphasis on generalisability and the ability to detect previously unseen and partially observable attacks. The absence of standardised approaches to address misbehaviours calls for advanced AI/ML-based solutions capable of handling the surging volume of data, enhancing robustness and generalisability, and meeting the real-time demands of V2X applications. To this end, we propose a generic deep RL (DRL) misbehaviour detection methodology capable of dynamically improving detection through interactions and experiences by leveraging various spatiotemporal behaviours present in the ambient vehicular measurement space. The scarcity of labelled vehicular data exacerbates the effective training of AI/ML-based models. Motivated by this challenge, we propose an ensemble learning framework for misbehaviour detection, coupled with unsupervised learning and a DRL model. This enables the detection of attacks from unlabelled vehicular data, facilitating the generalisation and detection of new and unseen attack variants. Additionally, addressing adversarial attacks poses a significant challenge, requiring enhanced solutions to make AI/ML-based misbehaviour detection more resilient against such threats. Towards this, we introduce and evaluate a tailored DRL approach designed to protect V2X communication systems against adversarial attacks, as well as mitigate issues stemming from inappropriate formatting of input training data due to vehicular sensor malfunctions or reading errors. By implementing data poisoning adversarial attacks, we demonstrate the resilience of the DRL-based misbehaviour detection approach even under severe conditions of sophisticated adversarial manipulation. Building upon the proposed DRL-based misbehaviour detection approach, we introduce a novel scheme for collaborative misbehaviour detection. This scheme involves deploying a DRL-based misbehaviour detection model in an RSU at the network edge. It leverages transfer learning principles to share the knowledge learned about misbehaviours at the source RSUs with the target RSU, enabling the reuse of relevant expertise for collaborative misbehaviour detection. Considering data poisoning attacks aimed at influencing misbehavior detection, we implement selective knowledge transfer from trustworthy RSUs to avoid adversarial interference. We introduce a semantic relatedness metric to quantify each RSU's trust level for collaborative misbehavior detection. |
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