Collaborative perception architecture in smart cities

Autonomous Driving Systems have become a reality in our society. Everyday, progress is made to increase vehicles' autonomy to drive without restrictions in roads and cities. To achieve that, researchers are always seeking for new methods to ensure the safety of the vehicles. A promising strategy is to improve the quality of the collected perception data as it directly influences the overall performance of the autonomous system. However, despite the advances achieved in detection methods and algorithms, perception is currently physically restricted by the available on-board sensors and their line-of-sight. To overcome this limitation, the autonomous system should not only capture on-board perception data, but also enhance it with data exchanged with other agents in the environment. This is known in research as Collaborative Perception, where mobile and stationary agents share object detection and sensor data inside an Intelligent Transport Systems network. This master's thesis brings together a collection of ETSI standards with the goal of developing a well-defined architecture for future implementation of a Secure Collaborative Perception Network in the context of Smart Cities. The architecture has been designed using the open-source software Capella Arcadia following a Model Based Software Engineering methodology

Identification of misbehavior detection solutions and risk scenarios in advanced connected and automated driving scenarios

The inclusion of 5G cellular communication system into vehicles, combined with other connected-vehicle technology, such as sensors and cameras, makes connected and advanced vehicles a promising application in the Cooperative Intelligent Transport Systems. One of the most challenging task is to provide resilience against misbehavior i.e., against vehicles that intentionally disseminate false information to deceive receivers and induce them to manoeuvre incorrectly or even dangerously. This calls for misbehaviour detection mechanisms, whose purpose is to analyze information semantics to detect and filter attacks. As a result, data correctness and integrity are ensured. Misbehaviour and its detection are rather new concepts in the literature; there is a lack of methods that leverage the available information to prove its trustworthiness. This is mainly because misbehaviour techniques come with several flavours and have different unpredictable purposes, therefore providing precise guidelines is rather ambitious. Moreover, dataset to test detection schemes are rare to find and inconvenient to customize and adapt according to needs. This work presents a misbehaviour detection scheme that exploits information shared between vehicles and received signal properties to investigate the behaviour of transmitters. Differently from most available solutions, this is based on the data of the on-board own resources of the vehicle. Computational effort and resources required are minor concerns, and concurrently time efficiency is gained. Also, the project addresses three different types of attack to show that detecting misbehaviour methods are more vulnerable to some profile of attacker than others. Moreover, a rich dataset was set up to test the scheme. The dataset was created according to the latest standardised evaluation methodologies and provides a valuable starting point for any further development and research

A comprehensive survey of V2X cybersecurity mechanisms and future research paths

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version

Analysis of security at the Near-real-time RIC xApps based on O-RAN-defined use cases

The Open Radio Access Network Alliance (O-RAN Alliance) is a group of industry and academic organizations that strive to realize the vision of next-generation cellular networks. Using standardized interfaces, telecommunications operators can operate multi-vendor infrastructure and deliver high-speed services to their mobile users. Additionally, the O-RAN Alliance has standardized an Open Radio Access Network (RAN) architecture based on the Third Generation Partnership Project (3GPP) and other standards. User planes and control planes are currently separate in RAN architecture. The separation makes it easier to accommodate network function virtualization methods required for 5G, enabling it to be more flexible. To help in the management of resources, the O-RAN standard proposes the use of xApps, i.e., dedicated applications that can be customly installed by the network operatior and that can be purchased from different vendors. For this reason, securely managing xApps represents a significant challenge for the security of the overall network.\\ In this thesis, we analyze the security of xApps and their proposed use cases. Based on the applications porposed by the O-RAN alliance, we provide an in depth analysis of the vulnerabilities and their impact on the network. We also discuss different features of attacks, such as reproducibility, stealthiness, exposure, and impact. Based on our analysis, we conclude that significant work is still to be made to guarantee the security of O-RAN and in particular of its xApps. This thesis hence provides a baseline for future research in the domain of security and privacy for next generation communication network

A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G has enabled Ultra-Reliable Low Latency Communications (URLLC) to CAVs. However, while communication performance has been enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure (PKI) proposed by standardization bodies cannot solely defend against these attacks. Thus, in complementary of that, sophisticated systems should be designed to detect such attacks and attackers. Machine Learning (ML) has recently emerged as a key enabler to secure future roads. Various V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. However, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper comprehensively surveys and classifies ML-based MDSs as well as discusses and analyses them from security and ML perspectives. It also provides some learned lessons and recommendations for guiding the development, validation, and deployment of ML-based MDSs. Finally, this paper highlighted open research and standardization issues with some future directions

A survey on vehicular communication for cooperative truck platooning application

Platooning is an application where a group of vehicles move one after each other in close proximity, acting jointly as a single physical system. The scope of platooning is to improve safety, reduce fuel consumption, and increase road use efficiency. Even if conceived several decades ago as a concept, based on the new progress in automation and vehicular networking platooning has attracted particular attention in the latest years and is expected to become of common implementation in the next future, at least for trucks.The platoon system is the result of a combination of multiple disciplines, from transportation, to automation, to electronics, to telecommunications. In this survey, we consider the platooning, and more specifically the platooning of trucks, from the point of view of wireless communications. Wireless communications are indeed a key element, since they allow the information to propagate within the convoy with an almost negligible delay and really making all vehicles acting as one. Scope of this paper is to present a comprehensive survey on connected vehicles for the platooning application, starting with an overview of the projects that are driving the development of this technology, followed by a brief overview of the current and upcoming vehicular networking architecture and standards, by a review of the main open issues related to wireless communications applied to platooning, and a discussion of security threats and privacy concerns. The survey will conclude with a discussion of the main areas that we consider still open and that can drive future research directions.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Robottiautojen tutkimukseen tarkoitetun virtuaalisen koneoppimisympäristön suorituskyvyn evaluointi

While automotive manufacturers are already implementing Autonomous Driving (AD) features in their latest commercial vehicles, fully automated vehicles are still not a reality. In addition to AD, recent developments in mobile networks enables the possibility of Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication. Vehicle-to-Everything (V2X) communication, or vehicular Internet of Things (IoT), can provide solutions that improve the safety and efficiency of traffic. Both AD and vehicular IoT need improvements to the surrounding infrastructure and vehicular hardware and software. The upcoming 5G network not only reduces latency, but improves availability and massively increases the amount of supported simultaneous connections, making vehicular IoT a possibility. Developing software for AD and vehicular IoT is difficult, especially because testing the software with real vehicles can be hazardous and expensive. The use of virtual environments makes it possible to safely test the behavior of autonomous vehicles. These virtual 3D environments include physics simulation and photorealistic graphics. Real vehicular hardware can be combined with these simulators. The vehicle driving software can control the virtual vehicle and observe the environment through virtual sensors, such as cameras and radars. In this thesis we investigate the performance of such simulators. The issue with existing open-source simulators is their insufficient performance for real-time simulation of multiple vehicles. When the simulation is combined with real vehicular hardware and edge computing services, it is important that the simulated environment resembles reality as closely as possible. As driving in traffic is very latency sensitive, the simulator should always be running in real-time. We select the most suitable traffic simulator for testing these multi-vehicle driving scenarios. We plan and implement a system for distributing the computational load over multiple computers, in order to improve the performance and scalability. Our results show that our implementation allows scaling the simulation by increasing the amount of computing nodes, and therefore increasing the number of simultaneously simulated autonomous vehicles. For future work, we suggest researching how the distributed computing solution affects latency in comparison to a real-world testing environment. We also suggest the implementation of an automated load-balancing system for automatically scaling the simulation to multiple computation nodes based on demand.Vaikka uusimmista automalleista löytyy jo itsestään ajavien autojen ominaisuuksia, robottiautot vaativat vielä runsaasti kehitystä ennen kuin ne kykenevät ajamaan liikenteessä täysin itsenäisesti. Robottiautojen ohella ajoneuvojen ja infrastruktuurin välinen (V2X) kommunikaatio ja tuleva 5G mobiiliverkkoteknologia sekä mobiiliverkkojen tukiasemien yhteyteen sijoitettavat laskentapilvet mahdollistavat liikenteen turvallisuuden ja sujuvuuden parantamisen. Tätä V2X kommunikaatiota voidaan esimerkiksi hyödyntää varoittamalla ajoneuvoja nurkan takaa tulevista pyöräilijöistä, jalankulkijoista ja huonoista tieolosuhteista. Robottiautojen ja V2X kommunikaation hyödyntämistä on hankala tutkia oikeassa liikenteessä. Fyysisten autojen ja tieverkostoa ympäröivän infrastruktuurin rakentaminen on kallista, lisäksi virhetilanteista johtuvat onnettomuudet voivat aiheuttaa henkilö- ja tavaravahinkoja. Yksi ratkaisu on virtuaalisten testausympäristöjen käyttö. Tällaiset simulaattorit kykenevät mallintamaan ajoneuvojen käyttäytymistä reaaliaikaisen fysiikkamoottorin avulla ja tuottamaan valokuvamaista grafiikkaa simulaatioympäristöstä. Robottiauton ohjelmisto voi hallita simuloidun auton käyttäytymistä ja havainnoida simuloitua ympäristöä virtuaalisten kameroiden ja tutkien avulla. Tässä diplomityössä tutkitaan liikennesimulaattorien suorituskykyä. Avoimen lähdekoodin simulaattorien ongelmana on niiden huono skaalautuvuus, eikä niiden suorituskyky riitä simuloimaan useita autoja reaaliajassa. Tässä diplomityössä tehdään lyhyt katsaus olemassa oleviin simulaattoreihin, joiden joukosta valitaan parhaiten yllämainittujen ongelmien tutkimiseen soveltuva simulaattori. Simulaattorin suorituskyvyn ja skaalautuvuuden parantamiseksi suunnitellaan järjestelmä, joka hajauttaa simulaattorin työkuorman useammalle laskentapisteelle. Kyseinen järjestelmä toteutetaan ja sen toimivuutta testataan mittaamalla. Mittaustulokset osoittavat, että hajautettu laskenta parantaa simulaattorin suorituskykyä ja että reaaliaikaisesti simuloitujen autojen lukumäärää voidaan kasvattaa lisäämällä laskentapisteiden lukumäärää. Jatkotutkimukseksi ehdotetaan tutkimaan simulaation hajauttamisen vaikutusta viiveisiin, ja kuinka simulaattorin aiheuttamat ylimääräiset viiveet suhtautuvat tosielämän viiveisiin. Lisäksi suositellaan automaattisen kuormituksentasaajan toteuttamista, jonka avulla simulaatiota voidaan automaattisesti hajauttaa useille laskentapisteille tarvittavan laskentakapasiteetin mukaisesti

On the Secure and Resilient Design of Connected Vehicles: Methods and Guidelines

Vehicles have come a long way from being purely mechanical systems to systems that consist of an internal network of more than 100 microcontrollers and systems that communicate with external entities, such as other vehicles, road infrastructure, the manufacturer’s cloud and external applications. This combination of resource constraints, safety-criticality, large attack surface and the fact that millions of people own and use them each day, makes securing vehicles particularly challenging as security practices and methods need to be tailored to meet these requirements.This thesis investigates how security demands should be structured to ease discussions and collaboration between the involved parties and how requirements engineering can be accelerated by introducing generic security requirements. Practitioners are also assisted in choosing appropriate techniques for securing vehicles by identifying and categorising security and resilience techniques suitable for automotive systems. Furthermore, three specific mechanisms for securing automotive systems and providing resilience are designed and evaluated. The first part focuses on cyber security requirements and the identification of suitable techniques based on three different approaches, namely (i) providing a mapping to security levels based on a review of existing security standards and recommendations; (ii) proposing a taxonomy for resilience techniques based on a literature review; and (iii) combining security and resilience techniques to protect automotive assets that have been subject to attacks. The second part presents the design and evaluation of three techniques. First, an extension for an existing freshness mechanism to protect the in-vehicle communication against replay attacks is presented and evaluated. Second, a trust model for Vehicle-to-Vehicle communication is developed with respect to cyber resilience to allow a vehicle to include trust in neighbouring vehicles in its decision-making processes. Third, a framework is presented that enables vehicle manufacturers to protect their fleet by detecting anomalies and security attacks using vehicle trust and the available data in the cloud