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In recent years, the scientific community has been focusing on deterministic Ethernet, which has helped drive the adoption of Time-Sensitive Networking (TSN) standards. Precision Time Protocol (PTP), specified in IEEE1588, is a TSN standard that enables network devices to be synchronized with a degree of precision that is noticeably higher than other Ethernet synchronization protocols. Generic Precision Time Protocol (gPTP), a profile of PTP, is designed to have low latency and jitter, which makes it suitable for industrial applications. However, like PTP, gPTP does not have any built-in security measures. In this work, we assess the efficacy of additional security mechanisms that were suggested for inclusion in IEEE 1588 (PTP) 2019. The analysis consists of implementing these security mechanisms on a physical gPTP-capable testbed and evaluating them on several high-risk attacks against gPTP.

Due to the promise of deterministic Ethernet networking, Time Sensitive Network (TSN) standards are gaining popularity in the vehicle on-board networks sector. Among these, Generalized Precision Time Protocol (gPTP) allows network devices to be synchronized with a greater degree of precision than other synchronization protocols, such as Network Time Protocol (NTP). However, gPTP was developed without security measures, making it susceptible to a variety of attacks. Adding security controls is the initial step in securing the protocol. However, due to current gPTP design limitations, this countermeasure is insufficient to protect against all types of threats. In this paper, we present a novel supervised Machine Learning (ML)-based pipeline for the detection of high-risk rogue master attacks.

Time Sensitive Network (TSN) standards are gaining traction in the scientific community and automotive Original Equipment Manufacturers (OEMs) due their promise of deterministic Ethernet networking. Among these standards, Generalized Precision Time Protocol (gPTP) - IEEE 802.1AS - allows network devices to be synchronized with a precision far higher than other synchronization standards, such as Network Time Protocol (NTP). gPTP is a profile of Precision Time Protocol (PTP) which, due to its robustness to delay variations, has been designated for automotive applications. Nonetheless, gPTP was designed without security controls, which makes it vulnerable to a number of attacks. This work reveals a critical vulnerability caused by a common implementation practice that opens the door to spoofing attacks on gPTP. To assess the impact of this vulnerability, we built two real gPTP-capable testbeds. Our results show high risks of this vulnerability destabilizing the system functionality.

With an ever-increasing number of vehicles roaming the streets and a general intensification of ongoing daily traffic the current vehicular safety systems are not able to reduce the number of traffic accidents further. As the majority of severe or deadly traffic accidents nowadays is caused by human error, car manufacturers and researchers alike focus on the self-driving vehicle as a promising solution to this problem, as a machine is unaffected from human conditions such as tiredness or drunk driving. To enhance the overall achievable driving safety and comfort the self-driving vehicles rely on an additional map database, besides the hardware sensor system installed onboard. The so-called High Definition Map (HD Map), a highly precise virtual model of the actual real-world provides detailed information about the ongoing traffic situation ahead of the car's sensor ranges. Otherwise critical traffic situations can be resolved by this a priori knowledge and if necessary, a handover of the driving control back to a human driver can be triggered. The maintenance of the HD Map is a major challenge, as due to the importance of the map for the self-driving vehicle map updates have to be realized in much shorter time (minutes instead of months) compared to established concepts common for human-oriented digital navigation maps. This thesis provides contributions in the areas of Distribution, Generation and Provision of such map updates, as the key communication challenges of the maintenance procedure. Our first contribution is the development, implementation and evaluation of a protocol that realizes the context-specific distribution of partial and incremental map updates. The protocol has been designed with the prerequisites and requirements of a self-driving vehicle in mind. To achieve the efficient dissemination of updates to all cars the protocol relies on infrastructure-based (cellular) and ad hoc communication (WLAN) between the vehicles. The performance of the protocol is evaluated based on realistic traffic simulations and actual map content. As our second contribution, we develop and implement an algorithm that detects changes in the road infrastructure (e.g. induced by construction sides) based solely on low-cost sensor information. This detection algorithm facilitates the succeeding update generation of the map data in the identified area. We evaluate the capabilities of the detection algorithm under a real-world data set in the example of a highway construction site scenario. To enhance the provision of map updates and vehicular sensor data via wireless communication, we conduct our third and most comprehensive contribution. We focus on the design and enhancement of a variety of different techniques and concepts to obtain broad knowledge about the serving wireless network to be provided in a subsequent step as valuable information to related transmission scheduling algorithms. These techniques and concepts include the measurement and prediction of the various performance indicators of actual deployed cellular networks, via low-cost hardware and software, as well as their further usage in simulation and network connectivity maps, always with an emphasis on easy deployability and the reutilization of existing components. Overall, this thesis presents essential contributions, which in their collectivity support the realization of a robust, dynamic and reliable maintenance cycle of an HD Map for self-driving vehicles.

This is a post-peer-review, pre-copyedit version of an article published in Communications in Computer and Information Science. The final authenticated version is available online at https://doi.org/10.1007/978-3-030-02907-4_11. Highly automated vehicles will change our personal mobility in the future. To ensure the safety and the comfort of their passengers, the cars have to rely on as many information regarding their current surrounding traffic situation, as they can obtain. In addition to classical sensors like cameras or radar sensors, automated vehicles use data from a so called High Definition Street Map. Through such maps, the vehicles are provided with continuous updates regarding their future driving environment on a centimeter accurate level. The required amount of data, which is necessary therefore, motivates the development of more efficient data transmission concepts. In this paper we present HD-Wmapan extension of our previous work the Dynamic Map Update Protocol. Based on each vehicle’s current context the Dynamic Map Update Protocol achieves a highly data efficient transmission of map updates compared to existing distribution approaches. HD-Wmapfurther reduces the costs of such transmissions by enabling map data to be shared via ad hoc communication between the vehicles. To evaluate the capabilities of HD-Wmapwe perform a first simulation of the morning commuting traffic within the area of Cologne, Germany. In this scenario HD-Wmapachieved an ad hoc map data off loading quota from cellular networks of up to 25.5%. These results demonstrate the gains of our approach to realize efficient map distribution via ad hoc communication, releasing load from wireless Internet access networks.