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.
By Konstantinos Gyftodimos, Kyriakos Chiotis, Elena Politi, George Dimitrakopoulos, Eirini Liotou
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.
By Kadir-Kaan \"Ozer, Ren\'e Ebeling, Markus Enzweiler
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.
By Araf Rahman, M Sabbir Salek, Mashrur Chowdhury
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.
By Romain Hermary, Samet Hicsonmez, Dan Pineau, Abd El Rahman Shabayek, Djamila Aouada
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.
By PengYu Chen, Shang Wan, Xiaohou Shi, Yuan Chang, Yan Sun, Sajal K. Das
arXiv:2607. 08978v1 Announce Type: new Abstract: Distributed IoT systems generate multivariate time-series streams for monitoring physical assets, servers, and embedded sensing platforms.
By Tung-Anh Nguyen, Van-Phuc Bui, Anh Tuyen Le, Kim Hue Ta, Minh Thuy Le, J. Andrew Zhang, Xiaojing Huang
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.
By Ammar Bouketta, Smail Niar, Hamza Ouarnoughi, Eva Mutuzo Brindle
arXiv:2608. 08906v1 Announce Type: new Abstract: Outlier detection in decentralized data environments is a challenging task for many machine learning implementations, particularly in settings where data cannot be shared.
By Mihailo Ili\'c, Milo\v{s} Savi\'c, Vladimir Kurbalija, Mirjana Ivanovi\'c, Giancarlo Fortino, Du\v{s}an Jakoveti\'c
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.
By Osman Tugay Basaran, Falko Dressler
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...
By Shashwat Khandelwal, Shanker Shreejith
While deep learning models have achieved state-of-the-art performance in time series anomaly detection, their complex architectures incur substantial inference overhead. Existing methods typically apply a uniform inference strategy across all data points, which is inefficient given that anomalies are inherently scarce and the vast majority of temporal data consists of predictable normal patterns.