Funktionen

Local Misbehavior Detection in Vehicle-to-Everything Communication
- An Edge Case Study

Context Description

Vehicle-To-Everything communication (V2X) is a system that enhances mobility, efficiency, and safety on the road by enabling the exchange of traffic specific data between vehicles, infrastructure, and road users (Intelligent Transport System-Stations; ITS-S). The evolution of V2X can be described in 3 steps: 1. Driving Awareness: Alerting the driver to problem scenarios out of sight or before reaction time 2. Cooperative Perception: Sharing sensory data with other vehicles and infrastructure 3. Coordinated Maneuvering: Controlling driving maneuvers with the help of V2X messages

Field Description

The cyber security and data privacy of the V2X system is already standardized. Trust between different entities in the V2X system is established by a Public Key Infrastructure. Consequently, message authenticity, integrity and sender privacy are protected. But security measures are not sufficient to rely on data from outside sources. The content of the data must also be plausible to guarantee safety in V2X use cases. For this thesis we grant security as given and won’t research this further in the domain of misbehavior. Misbehavior is the “act by an ITS-S of transmitting false or misleading information, or information that was not authorized by the local policy, either purposefully or unintendedly” (additional information 2). Although V2X will start to be implemented in the coming years on the first evolutionary step, misbehavior detection is not yet fully standardized. There are few standards for a global view of misbehavior detection and reporting finished or work in progress, but none that address concrete proposals for misbehavior detection in a vehicle. There exist only a few research papers on this topic.

Task Description

The goal of the master thesis is to provide an overview of the current state of V2X misbehavior. That means to define misbehavior according to current standards and to discuss working algorithms for local misbehavior detection under current standardization. Another goal of the work is to investigate how present algorithms deal with edge cases not only from day 1 use cases of V2X misbehavior but also exemplarily from day 2 and 3 use cases (see evolutionary steps).

Additional Information

1. https://auto-talks.com/v2x-evolution-part-1-evolving-v2x-applications-and-their-implications/
2. https://www.etsi.org/deliver/etsi_ts/103700_103799/103759/02.01.01_60/ts_103759v020101p.pdf
3. https://arxiv.org/abs/2112.02184
4. https://www.etsi.org/deliver/etsi_tr/103400_103499/103460/02.01.01_60/tr_103460v020101p.pdf

Overview of Possible Subtasks

1. Collect algorithms for local misbehavior detection in V2X
2. Evaluate how well the algorithms would still work under current standardization
3. Definition of different relevant edge cases based on V2X use cases from different evolutionary implementation steps
4. Discuss how to properly handle these edge cases
5. Investigate how well the algorithms would handle them

Organisatorisches:

Aufgabensteller:
Prof. Dr. D. Kranzlmüller

Dauer der Arbeit:

• Masterarbeiten: 6 Monate

Anzahl Bearbeiter: 1

Betreuer:

• Prof. Dr. D. Kranzlmüller
• Stephan Rein (MSG Group)
• Fabian Dreer