Intrusion Detection Systems (IDSs) serve as a crucial defense mechanism against cyberattacks targeting the In-Vehicle Network (IVN) of modern, interconnected vehicles. To develop and test new IDS approaches, researchers require realistic IVN data featuring real attacks on moving vehicles. To this end, this paper presents Controller Area Network and Automotive Ethernet Realistic Data Set (CarDS), a novel dataset targeting both the Controller Area Network (CAN) and Automotive Ethernet (AE) traffic of a modern, multi-domain and multi-protocol IVN. Existing datasets are often simulated or limited to basic IVN architectures consisting of only a single CAN bus. Additionally, there are no realistic datasets for AE, despite its growing importance in high-speed in-vehicle communication. CarDS addresses these limitations by providing a labeled, time-synchronized dataset of CAN and AE traces that includes both comprehensive benign profiles and sophisticated attacks. Our traces are captured from an electric vehicle from 2020 featuring a domain-oriented architecture comprising 10 internal CAN buses and 6 AE buses. Specifically, our dataset covers 9h 07m 09s of real IVN data and features 397,383,125 CAN and 180,604,377 AE messages distributed over different scenarios in 258 traces.

Diagnostic protocols for vehicles are important for maintenance, updates, etc. However, if they are not secure, an attacker can use them as an entry point to the vehicle or even directly access critical functionality. In this paper, we discuss the security of the vehicle diagnostics protocols Diagnostics over IP (DoIP) and Unified Diagnostic Services (UDS). For UDS, we provide a formal analysis of the included security protocols SecurityAccess service and the different variants of the new Authentication service introduced in the year 2020. We present two new vulnerabilities, we identified in our analyses, describe how they can be mitigated and formally verify our mitigations. Furthermore, we give recommendations on how to securely implement UDS and how future standards can be improved.

The Electric Vehicle (EV) charging architecture consists of several actors which communicate with different protocols. A serious issue is the lack of adequate privacy-preserving measures that enables the generation of movement profiles or inferring consumer habits by all of the involved actors. In this paper, we propose an extension of a Trusted Platform Module (TPM)-based Direct Anonymous Attestation (DAA) scheme to enable privacy-preserving charging authorization and billing. Our implementation shows that our solution can be easily integrated into existing protocols of the Plug-and-Charge (PnC) EV charging architecture and introduces only minor overhead. The formal analysis using the Tamarin prover shows the security and privacy of our protocol extension.

Automotive Ethernet is increasingly used in modern vehicles and complements or replaces legacy bus systems such as CAN. Ethernet also enables service-oriented communication with the Scalable service-Oriented MiddlewarE over IP (SOME/IP) middleware. In this paper, we present a formal and practical security analysis of Scalable service-Oriented MiddlewarE over IP (SOME/IP), the identified Man-in-the-Middle (MITM) attacks, and propose two security extensions. The attacks are possible even if SOME/IP is used in combination with link layer security mechanisms. The attacker can impersonate a service offering server and a service consuming client. The two most common communication methods, request/response and publish/subscribe, are both vulnerable. In most communication scenarios, we are able to route all messages over the attacker. Our security extensions for authentication and authorization of service provisioning and usage protect against these attacks. We formally analyze the security and evaluate the overhead with practical implementations.