The paper introduces a privacy‑preserving anomaly detection framework for in‑vehicle networks that uses a Temporal Transformer CAN Encoder with Federated Lightweight Heads. It captures subtle temporal and contextual irregularities in CAN bus traffic by learning signal evolution over time, while federated learning allows multiple vehicles or ECUs to collaboratively improve a shared model without sharing raw data. Experiments on open‑source datasets demonstrate robust detection performance with maintained efficiency and privacy.
Modern vehicles rely on large numbers of Electronic Control Units (ECUs) that constantly exchange information over the Controller Area Network (CAN) bus. Due to the rapidity, structure, and repetition...
arXiv:2511. 15339v3 Announce Type: replace-cross Abstract: Automotive telemetry data exhibits slow drifts and fast spikes, often within the same sequence, making reliable anomaly detection challenging.
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...
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.
arXiv:2604. 13924v3 Announce Type: replace-cross Abstract: Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous anomalies and the scarcity of labelled data.
arXiv:2603. 26842v3 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems.
arXiv:2502. 09194v1 Announce Type: cross Abstract: Generative Artificial Intelligence (AI) techniques have become integral part in advancing next generation wireless communication systems by enabling sophisticated data modeling and feature extraction for enhanced network performance.
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:2607. 08978v1 Announce Type: new Abstract: Distributed IoT systems generate multivariate time-series streams for monitoring physical assets, servers, and embedded sensing platforms.
The paper introduces the Temporal Cycle-Aware Attention Autoencoder with Cross-Signal Consistency (TCAA‑CS) for detecting anomalies in railway passenger door operations. It treats each full opening‑dwell‑closing cycle as a monitoring unit and trains on nominal cycles using a dual‑stream encoder for physical measurements and logical states, an LSTM with temporal attention, and a hybrid anomaly score that fuses reconstruction error, latent‑space deviation, and phase‑aware cross‑signal consistency. On real industrial data, TCAA‑CS achieves 93.8% recall, 97.3% precision, and a 0.5% false‑alarm rate, outperforming other unsupervised baselines and demonstrating real‑time feasibility on an NVIDIA Jetson AGX Xavier.
arXiv:2610.07489v1 Announce Type: cross Abstract: Increasing connectivity to the outside world and the lack of inbuilt security mechanisms have made legacy intra-vehicular networks vulnerable to cybe...
arXiv:2606. 30430v1 Announce Type: cross Abstract: The increasing connectivity of modern vehicles has made securing in-vehicle communication networks a critical challenge.