> [!ABSTRACT] > [5G-INSIGHT](https://www.list.lu/en/informatics/project/5g-insight/) is a research project focused on developing intelligent, orchestrated, security and privacy-aware slicing for 5G and beyond vehicular networks. The project aims to enhance vehicular communication systems by leveraging network slicing and advanced security mechanisms. > [!FACTSHEET] > - **Funding**: Co-funded by the Agence Nationale de la Recherche (ANR – FR) and the Fonds National de la Recherche (FNR – LU) > - **Duration**: 2021-2024 > - **My role**: Initiator - Led the proposal development and consortium setup on LIST side, delegating project execution once the project was acquired. > - **Key partners**: University of Luxembourg, Université Gustave Eiffel, La Rochelle Université, Université de Bourgogne ## Context The automotive industry is experiencing significant technological advancements in vehicular communication capabilities (V2X – Vehicle-to-Everything) and automated driving functions ([[Connected and Automated Mobility]]). These developments are closely linked to the deployment of 5G technologies and service-oriented architectures enabled by network slicing. Network slicing allows the partitioning of physical 5G networks into multiple virtual networks, each tailored to specific use cases with distinct capabilities. This approach provides personalised network services that adapt to the varying needs of connected applications, such as latency, bandwidth, scalability, and resource allocation. However, the deployment and adoption of these technologies raise concerns regarding data security and privacy, necessitating advanced solutions to detect and mitigate potential attacks and anomalies in vehicular 5G networks. ## Key objectives 1. **Develop advanced security mechanisms**: Create solutions to detect and mitigate attacks inherent to network slicing in 5G vehicular networks. 2. **Utilise machine learning algorithms**: Employ advanced machine learning techniques to predict and detect attacks and anomalies in vehicular 5G networks. 3. **Generate realistic datasets**: Produce realistic datasets on vehicular slicing attacks to develop reliable prediction models. ## Outcomes - **Enhanced security in 5G vehicular networks**: Implementation of advanced security mechanisms to protect against network slicing attacks. - **Improved attack detection**: Development of machine learning-based techniques for accurate prediction and detection of network anomalies. - **Comprehensive datasets**: Creation of realistic datasets to support ongoing research and development in network security. ## Consortium 5G-INSIGHT is a collaborative project co-funded by the Agence Nationale de la Recherche (ANR – FR) and the Fonds National de la Recherche (FNR – LU), involving key partners in the field of vehicular communication and network security. Visit the [5G-INSIGHT website](https://5g-insight.eu/) for more information.