arXiv:2606. 05779v1 Announce Type: cross Abstract: Autonomous spacecraft require rapid, lightweight, and reliable onboard detection of cyber-RF threats.
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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:2605. 13922v2 Announce Type: replace-cross Abstract: During thDuring the last few years, the term Mechanistic Interpretability, a specific area, under the umbrella of explainable artificial intelligence (XAI), has been introduced, to explain the decisions made by complex machine learning (ML) models in critical systems like UAV intrusion detection systems (UAVIDS).
arXiv:2606. 18454v1 Announce Type: cross Abstract: We present Veriphi, a GPU-accelerated neural network verification system that combines fast adversarial attacks with formal bound certification using alpha,beta-CROWN methods.
The paper proposes a new unsupervised safety detection method for large language models that relies on anomaly detection rather than supervised training on unsafe data. By leveraging local sparsity in a linear representation space obtained via a sparse autoencoder, the authors develop a framework for locally masked SAE-based anomaly detection, providing theoretical support and empirical validation across multiple architectures and datasets. When calibrated with only 1% out-of-distribution data, the method achieves near‑optimal performance while using just 1–2% of SAE neurons for computation.
The paper introduces the Temperature Scaling Attack (TSA), a training‑time method that degrades model confidence calibration while keeping predictive accuracy largely intact. TSA injects temperature scaling with a learning‑rate coupling during local federated training, shifting confidence scores and causing significant calibration errors (e.g., a 145% increase on CIFAR‑100) with less than a 2% drop in accuracy. The authors provide a convergence analysis for non‑IID settings and demonstrate TSA’s effectiveness across three benchmarks, robust aggregation, and post‑hoc calibration defenses, highlighting its impact on mission‑critical systems such as healthcare verification and autonomous driving.
arXiv:2604. 02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data.