arXiv AI

A 6G Integrated Sensing and Communication Framework for Railway Intrusion Detection and Collision Prediction

arXiv:2608. 04710v1 Announce Type: cross Abstract: Integrated Sensing and Communication (ISAC) combines sensing and communication to efficiently utilize wireless resources and is emerging as a key paradigm for next-generation wireless networks.

arXiv Machine Learning

Jul 13

XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles

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).

By Iakovos-Christos Zarkadis, Christos Douligeris

safety

arXiv Machine Learning

Jun 30

In-Vehicle Digital Twin-Based Collision Warning Framework with Sybil Attack Detection

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.

By Mohammad Imtiaz Hasan, Abyad Enan, Jean Michel Tine, Araf Rahman, M Sabbir Salek, Mashrur Chowdhury

safety

arXiv Machine Learning

Jun 2

Measurement-Driven Early Warning of Reliability Breakdown in 5G NSA Railway Networks

arXiv:2511. 08851v5 Announce Type: replace-cross Abstract: This paper presents a measurement-driven study of early warning for reliability breakdown events in 5G non-standalone (NSA) railway networks.

By Po-Heng Chou, Da-Chih Lin, Hung-Yu Wei, Walid Saad, Yu Tsao

llms benchmarks

arXiv Machine Learning

Jun 5

Hybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS2018

arXiv:2606. 05714v1 Announce Type: cross Abstract: Digital infrastructure is growing at a rapid pace in the United States, and as a result, exposure to advanced cyber threats to critical sectors including healthcare, finance, transportation, energy and government systems is growing.

By Md. Iqbal Hossan, Md. Serajul Kabir Chowdhury Rubel, Md. Arifur Rahman, B. M. Taslimul Haque

arXiv Machine Learning

Aug 5

Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks

arXiv:2608. 03319v1 Announce Type: cross Abstract: Future integrated sensing and communication (ISAC) architectures separate the sensing entity (SE) that acquires measurements from the sensing function (SF) that performs inference, creating a need for compact, task-oriented feedback on the SE-SF interface.

By Shiv Shankar, Radha Krishna Ganti, J Klutto Milleth

rag efficiency

arXiv Machine Learning

Jul 8

6G Sensing Security: Distributed Game-Theoretic RL for Urban Beamforming and Attacker Detection

arXiv:2607. 06115v1 Announce Type: cross Abstract: In next-generation networks, communication systems will no longer be limited to data transmission and will be expected to acquire awareness of the surrounding environment.

By Parmida Geranmayeh, Onur G\"unl\"u

reinforcement-learning

arXiv AI

Jun 24

Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection

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.

By Konstantinos Kalogiannis, Ahmed Mohamed Hussain, Hexu Li, Panos Papadimitratos

llms efficiency benchmarks safety

arXiv Machine Learning

Jun 25

A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids

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.

By Sajib Debnath, Remon Das

efficiency benchmarks

arXiv Machine Learning

Jul 7

Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios

arXiv:2607. 02537v1 Announce Type: cross Abstract: Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity.

By Nisha L. Raichur, Lucas Heublein, Dominik Seu{\ss}, Frank Deinzer, Felix Ott

arXiv Machine Learning

Jun 11

Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems

arXiv:2606. 12075v1 Announce Type: cross Abstract: Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks.

By Mayank Raj, Nathaniel D. Bastian, Lance Fiondella, Gokhan Kul

safety

arXiv Machine Learning

Jun 9

Balancing Real and Synthetic Data for CNN-based Masonry Crack Detection

arXiv:2606. 08033v1 Announce Type: cross Abstract: Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages.

By Mattia Forlesi, Alfonso Esposito, Ivan Zyrianoff, Alessandro Marzani, Marco Di Felice

arXiv Machine Learning

Jul 2

Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence

arXiv:2607. 00763v1 Announce Type: cross Abstract: Digital forensic investigations of network intrusions require analytical outputs that are traceable, reproducible, and court-defensible - requirements existing machine learning pipelines do not satisfy, since they treat original evidence as training data and produce opaque classifications without instance-level justification.

By Jose Luis Vela Alonso, Carmen Pellicer

safety

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