The paper presents a deep‑learning approach for detecting cyberattacks in Low‑Earth Orbit satellite systems, leveraging the UNSW‑IoTSAT dataset. It explores structured architectures that preserve hardware, orbital, and radio‑frequency data, including a Subsystem‑Fusion MLP and a hierarchical multimodal Transformer that captures cross‑subsystem interactions and temporal dynamics. Experiments show that the hierarchical Transformer achieves up to 91.66% accuracy and 85.63% macro F1 under a leakage‑resistant evaluation protocol, highlighting the importance of multimodal modeling and rigorous testing.
arXiv:2606. 05779v1 Announce Type: cross Abstract: Autonomous spacecraft require rapid, lightweight, and reliable onboard detection of cyber-RF threats.
arXiv:2606. 25200v1 Announce Type: new Abstract: The accelerated digitalization of renewable energy smart grids through IoT sensors, AMI, and SCADA systems has significantly expanded the attack surface for sophisticated cyberattacks, FDI attacks that stealthily distort state estimation and DoS/DDoS attacks that flood communication channels.
arXiv:2512. 07925v4 Announce Type: replace-cross Abstract: Ongoing armed conflict in Sudan highlights the need for rapid monitoring of conflict-related fire-affected areas.
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:2601.00900v2 Announce Type: replace-cross Abstract: As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target re...
arXiv:2609.01514v1 Announce Type: cross Abstract: Historically, retrieving rainfall data from satellite imagery has been the domain of space agencies. However, in recent years, the development of che...
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.
arXiv:2606. 11098v1 Announce Type: cross Abstract: Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near-perfect performance on CIC-IDS2017.
The paper presents a marine event detection pipeline for Earth observation satellites that uses a self‑supervised neural network encoder to compress images into a latent space, followed by a machine learning anomaly detection model to spot deviations from normal sea patterns. It compares this approach with traditional algorithms such as Isolation Forest, One‑Class Support Vector Machine, and Local Outlier Factors, demonstrating superior performance. The lightweight, resource‑efficient design is tailored for satellites with limited computational resources and has been integrated into missions like ESA’s Phisat‑2 and Microsoft/Thales Alenia Space IMAGIN‑e, enabling timely alerts and efficient data reduction.
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.
arXiv:2512. 22179v3 Announce Type: replace Abstract: Detecting previously unseen attacks remains a major challenge for machine learning-based intrusion detection systems.
arXiv:2602. 01359v3 Announce Type: replace-cross Abstract: Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios.