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Das Kapitel befasst sich mit dem SELFY-Projekt, das darauf abzielt, die Cybersicherheit und Widerstandsfähigkeit in der vernetzten Mobilität (CCAM) zu stärken. Es stellt die SELFY-Toolbox vor, eine Sammlung innovativer Werkzeuge, die darauf ausgelegt sind, Datenintegrität, Bedrohungserkennung und sichere Aktualisierungen innerhalb des CCAM-Ökosystems zu adressieren. Der Werkzeugkasten besteht aus drei Hauptkategorien: situationales Bewusstsein und kollaborative Wahrnehmung (SACP), kooperatives Resilienz- und Heilsystem (CRHS) und Vertrauens- und Datenmanagementsystem (TDMS). Jede Kategorie umfasst eine Reihe von Werkzeugen, die zusammenarbeiten, um die allgemeine Sicherheit und Robustheit von CCAM-Systemen zu verbessern. Das Kapitel diskutiert auch das Marktpotenzial von SELFY und skizziert die nächsten Schritte zur Validierung seiner Lösungen durch Simulation, Labortests und Tests in der realen Welt. Die Ergebnisse dieser Validierungen werden auf Grundlage der für den CCAM-Sektor relevanten Key Performance Indicators (KPIs) bewertet.
KI-Generiert
Diese Zusammenfassung des Fachinhalts wurde mit Hilfe von KI generiert.
Abstract
SELFY envisions an agnostic toolbox for the self-management of security and resilience of the CCAM (Connected, Cooperative and Automated Mobility) ecosystem, which can be easily deployed to extend the current Operational Design Domain (ODD), providing self-awareness, self-resilience and self-healing mechanisms and enhancing trust between stakeholders. SELFY is based on four pillars: Situational awareness, Resilience, Secure Data Sharing and Trust and provides three groups of tools. SACP (Situational Awareness and Collaborative Perception) tools aim at providing all CCAM actors with a comprehensive understanding of their environment, i.e., the perception of objects, such as other traffic participants and stationary objects. CRHS (Cooperative Resilience and Healing System) tools enable self-protection actions whenever a compromising situation is detected in relation to assets, vehicles, operations, or the system itself. TDMS (Trust and Data Management System) tools establish a secure and trusted environment for data in a collaborative and cooperative context, both for infrastructure and assets, as well as for citizen’s data, such as drivers or pedestrians with special attention to privacy considerations. By defining a collaborative environment between the different tools to respond to new threats, risks and attacks SELFY facilitates the comprehension of new challenges in the cybersecurity aspect of CCAMs.
1 Introduction and Motivation
Cooperative Connected Automated Mobility (CCAM) will improve the mobility ecosystem of millions of citizens in Europe and around the world. Leveraging artificial intelligence solutions, big data, enhanced connectivity, and digitalization, CCAM will pave the way for smarter cities, enhancing their efficiency, sustainability, and accessibility for all users. However, as defined by the CCAM’s SRIA [1] (Strategic Research and Innovation Agenda), Key Enabling Technologies such as cybersecurity are required to provide robustness and resilience. Threats, incidents, and malicious actions as well as system failures must be detected in real-time and reported to a decision-making system to perform response actions. CCAM systems’ resilience heavily relies on the data and information fusion, sharing and processing. Therefore, guaranteeing a secure flow of generated and processed data between all stakeholders is vital for a correct, efficient, and robust operation of the different services and systems in the CCAM environment. Ensuring the veracity, quality and integrity of the generated data is essential in the mobility management and control process, both in the stages of persistence, transmission, access and use of information, as well as in the different stages of cybersecurity and security management of the CCAM ecosystem.
There are already some regulations and standards like the UNECE WP.29 R155 [2] and R156 [3] or the ISO/SAE21434 [4] which focus their efforts on providing a more complete cybersecurity management system with incident management and remote software updates as well as defining a framework for including cybersecurity requirements and risk management. SELFY tools like the Vehicle Security Operations Center (VSOC) or Secured Over the Air software update (SOTA) provide a partial solution but go beyond by proposing a collaborative environment between all CCAM elements by sharing and fusing data and increasing the awareness, robustness and trust of the overall CCAM system.
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2 SELFY Concept
SELFY researches and implements a set of tools that are agnostic to OEMs and suppliers, being easily integrated (ready-to-use and re-use) in service and product development processes from the design stage (resilience by design), considering the full ODD of the different CCAM scenarios. SELFY toolbox will perform a continuous assessment of the robustness and resilience, based on a process of situational awareness through collaborative perception, a set of cooperative resilience and healing systems services in response to compromised situations (cyber-attack, intrusion, cyber-terrorism or cyber-sabotage), based on the gathering and sharing of data in a trusted collaborative environment. The key technologies underpinning the SELFY toolbox are described in Fig. 1. These technologies are grouped into three categories which in turn are supported by the four cybersecurity innovation pillars outlined earlier. To address the complexity of CCAM the SELFY tools are deployed on multiple layers, e.g., vehicle, RSU or cloud.
2.1 Situational Awareness and Collaborative Perception (SACP)
The SACP includes a set of tools to obtain a comprehensive understanding of the environment, perception and position of objects by using artificial intelligence and aggregating and fusing data from sensors and V2X.
Vehicle
Situational Assessment Module. It detects anomalies, misuse, malfunctions, etc. The tool takes the environment model (fused from RSU and on-board vehicle sensor data), ego vehicle CAN inputs, ego vehicle trajectory, Cooperative Awareness Messages (CPMs) as input and analyzes the data using AI-Based methods to detect anomalies and assign a risk level to the current situation.
Vehicle-centered situational awareness tool. It provides a 360º view of the vehicle surroundings using sensor fusion and AI-based object (vehicle and VRU) detection algorithms. This information will be shared to the on-board V2X communication module, and it will be used to generate a Local Dynamic Map (LDM). The LDM will be shared with RSU and other vehicles through V2X channel.
Aggregation tool. CAMs and CPMs shared from traffic participants and infrastructure via V2X communications are aggregated and fused with the local perception information into a central perception system.
RSU
Traffic Monitoring Tool. It identifies and tracks objects based on video data, results of sensors fusion and V2X messages.
Threat Evaluation Tool. It identifies different types of threats and makes decisions about how to handle them.
Sensor Fusion & Anomaly Detection Tool. It analyses and fuses data from LIDARs/RADARs mounted on the RSU. The tool also aims to detect anomalies between a pair of sources.
2.2 Cooperative Resilience and Healing System (CRHS)
The CRHS includes a set of tools that will elicit self-protection whenever a compromised situation is detected. The actions can be taken locally or in cooperation at global CCAM level. This global capacity is embodied in the VSOC.
Vehicle
Safety Operational Tool. It evaluates the situational and own risks of the ego-vehicle and modifies the planned trajectory to a minimum risk manoeuvre when needed in the events of system failures such as localization losses.
Artificial Immune System. It provides a mechanism to detect deviations. If these involve an attack, learn from them and (if possible) mitigate this type of attack. It acts as a swarm that connects all vehicles, updating and sharing the mitigation’s solutions.
ROBUST tool. It provides a set of algorithms that allows designing and implementing physics-based (kinematics/dynamics) schemes for trajectory planning, anomaly detection, and communication protocols that are optimized by design to be as robust as possible (in terms of potential damage to vehicles/infrastructure) against cyberattacks.
RSU
Audit Box. It audits the WiFi and BT interfaces of the vehicle and detects jamming situations. It logs the results and sends it to the VSOC.
Cloud
VSOC. It collects data from the SELFY toolset (through the VSOC API) and detects anomalies within the data of the CCAM ecosystem. The analysis results are prepared and distributed to the SELFY tools through the VSOC API. The API can be used to subscribe to information (e.g., trust score for tool partners and OEMs) and send data to the VSOC.
Interaction-based V&V (Verification and Validation tool. It is dedicated to conducting formal analyses employing models of interactions akin to UML Sequence Diagrams and Message Sequence Charts. Among the analysis features offered by the tool, we have Runtime Verification (RV), as detailed in [5] and in [6]. This functionality enables the identification of non-conformities against the interaction models, thereby exposing potential security threats within V2X communication flows in CCAM systems.
2.3 Trust Data Management System (TDMS)
TDMS includes a set of tools addressed to build a secure and trusted environment for data in a collaborative context both for infrastructure and vehicles as well for citizens (drivers or pedestrians).
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Vehicle
Sensitive Data Anonymization tool. It receives the detected objects (vehicle and VRU) from the vehicle-centred situational-awareness tool, detects the sensitive data (human faces and vehicle license plates), and anonymizes such personal and sensitive data by blurring.
Privacy Utility Tool. It distorts (to enforce privacy) data used for platooning trajectory planning and anomaly detection before sharing via V2X so that sensitive information is hidden as much as possible while preserving data utility.
RSU
V2X Privacy tool. It assigns IDs for describing tracked objects in CPM messages, the assignment of said IDs taking into account change of identity by CAM-sending vehicles so as to avoid leaking vehicle identity through CPM messages.
Roadside Trust Tool. It assesses a level of trust for each V2X agent based on V2X messages and data from roadside sensors.
Cloud
SOTA tool. It provides methods to ensure secure SW updates for connected and automated vehicles. It focuses on the entire vehicle level and not only on its key components. The entire life cycle of the vehicle within the CCAM shall be addressed at each layer of the CCAM system.
All
PQC provides libraries to use Post Quantum Cryptography as part of a TLS function. Post Quantum Cryptography guarantees that systems will be robust against current and future cyberattacks on crypto-systems
Remote Attestation System Box. It aims at ensuring integrity verification of various software components running on the vehicle or the RSU.
Key Exchange/Management Box (KEMS Box). It dynamically generates and updates, whenever needed, the keying material for all the entities that require it for different purposes like data protection, network access control.
ITS Station. It transmits and receives V2X messages in compliance with the V2X standards. V2X communication can happen either in direct mode (ITS-G5) or through cellular communication (with a relay in the cloud: C-ITS-S) (Not planned in SELFY). It can also route standard IP(v4/v6) messages between components and the infrastructure on the Internet.
3 Market Potential
SELFY’s significance relates to its potential impact in security, safety, and resilience of the CCAM ecosystem, such as OEMs, traffic management centers, TIER-x suppliers, smart infrastructure suppliers or regulatory and standardization bodies, among others. The market segments are very broad, from automotive OEMs and suppliers, traffic and infrastructure management, road operators to product and service development teams. Considering the new regulatory requirements in CCAM, the market potential is evident, which is estimated [6] at EUR 535 million. It can be reached through three main sources: licensing and subscription fees for using the SELFY toolbox, consultation and customization services for specific needs and maintenance and support fees.
4 Conclusions and Next Steps
Cooperative Connected and Automated Mobility requires cybersecurity and privacy tools. New regulations and standards are coming and solutions have to be deployed over the system. Citizens, such as drivers, Vulnerable Road Users (VRU) or pedestrians shall accept and trust the system. SELFY’s solution is a cooperative toolbox which addresses all of these issues, increasing perception and awareness, robustness, attack detection and mitigation rate and providing safe states and secure updates, everything orchestrated by a VSOC.
As a next step, SELFY is going to be validated over three main use cases: Resilient Cooperative Mechanisms for VRU Safety, Secure empowerment of backend system for traffic management system and Robust platooning. These validations are going to be separated into three groups: simulation, laboratory and real-world validation and the outcomes of the project are going to be evaluated according to a set of KPIs (Key Performance Indicators) relevant to the CCAM sector.
Acknowledgement
The research leading to these results has received funding from the European Union’s Horizon Europe programme under grant agreement No 101069748- SELFY project.
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SELFY - Self Assessment, Protection and Healing Tools for a Trustworthy and Resilient CCAM
Verfasst von
Victor Jimenez
Mario Reyes de Los Mozos
Pau Perea Paños
Paula Cecilia Fritzsche
Kevin Gomez Buquerin
Tina Volkersdorfer
Hans-Joachim Hof
Christophe Couturier
Thierry Ernst
Miao Zhang
Mohamed Saied Mohamed
Mario Rodríguez-Arozamena
Iñigo Aranguren-Mendieta
Joshué Pérez
Adrien Jousse
Carlos Murguia
Nathan van de Wouw
Romain Bellessort
Behzad Salami
Aleksandar Jevtić
Boutheina Bannour
Manel Rodríguez Recasens
Isaac Ropero
Burcu Ozbay
Ali Eren
Mustafa Bektas
Deryanur Tezcan
Christoph Pilz
Sarah Haas
Gernot Lenz
