The paper presents a digital‑twin (DT) based intrusion detection system (IDS) for vehicle powertrain CAN bus traffic, modeling physical relationships among decoded signals to detect payload‑manipulation attacks that preserve normal timing and sequencing. Using a shared‑encoder LSTM trained on 17 Hyundai/Kia CAN signals, the DT flags anomalies when residuals exceed a threshold, achieving high detection rates (up to 94.6%) for stealthy attacks such as continuous drift and masquerade, while a range‑and‑plausibility baseline fails to detect them. The study demonstrates that learning coupled vehicle dynamics enables detection of payload‑level attacks that evade traditional timing‑based IDSs, though false positives remain a challenge.
Existing automotive intrusion detection systems (IDSs) for the Controller Area Network (CAN) largely target discrepancies in message timing, frequency, or sequencing and cannot detect attacks that pre...
arXiv:2608. 05548v1 Announce Type: cross Abstract: Modern vehicles rely on the Controller Area Network (CAN) bus, whose design prioritizes low cost and real-time performance but provides no message authentication or encryption.
arXiv:2606. 18599v1 Announce Type: cross Abstract: The Controller Area Network (CAN) protocol is the primary communication standard for Electronic Control Units (ECUs) in modern vehicles, but its lack of encryption and authentication exposes it to a range of security threats.
The paper introduces SPADE, a labelled, multi‑modal dataset for detecting attacks on Signal Phase and Timing (SPaT) messages from the perspective of connected vehicles. Generated via Eclipse MOSAIC, SPADE includes 1.89 million timestep records across six attack classes and one benign class, combining SPaT fields, camera confidence scores, and V2V peer data over 40 features. The dataset, along with generation code and scenario configurations, is publicly released on GitHub to enable reproducible deep‑learning intrusion detection research in C‑V2X security.
SPADE is a labelled, multi‑modal, simulation‑based dataset for detecting attacks on Signal Phase and Timing (SPaT) messages from the perspective of connected vehicles. It contains 1.89 million timestep records generated by injecting six classes of application‑layer attacks and one benign class into the SAE J2735 SPaT protocol, across multiple intersection geometries, operating conditions, and random seeds. Each record fuses SPaT fields, onboard camera confidence scores, and cooperative V2V peer data into 40 features, enabling deep‑learning intrusion detection systems to distinguish deliberate attacks from environmental noise.
arXiv:2606. 28625v1 Announce Type: cross Abstract: Connected Vehicles (CVs) rely extensively on communication technologies to enable data-driven predictive analyses for enhancing performance and safety.
arXiv:2608. 16159v1 Announce Type: cross Abstract: Digital Twins (DTs) are increasingly used to monitor and analyze Cyber Physical Systems (CPS).
arXiv:2606. 30430v1 Announce Type: cross Abstract: The increasing connectivity of modern vehicles has made securing in-vehicle communication networks a critical challenge.
arXiv:2601. 01701v2 Announce Type: replace-cross Abstract: Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, and efficiency of industrial systems.
arXiv:2607. 28665v1 Announce Type: cross Abstract: Automated driving systems (ADSs) are becoming ubiquitous.
arXiv:2512. 15503v3 Announce Type: replace-cross Abstract: Vehicular platooning promises transformative improvements in transportation efficiency and safety through the coordination of multi-vehicle formations enabled by Vehicle-to-Everything (V2X) communication.
arXiv:2608. 11286v1 Announce Type: cross Abstract: Cyberattack detection in electric vehicle charging infrastructure is complicated by legitimate post-activation revisions to requested energy and departure time.