arXiv:2606. 03381v1 Announce Type: cross Abstract: Ensuring the protection of Artificial Intelligence (AI) models deployed in military Command and Control (C2) systems and critical infrastructure is essential for maintaining information superiority.
By Maxime Schwarzer, Johannes F. Loevenich, Gustavo S\'anchez, Laurin Holz, Thies M\"ohlenhof, Tobias H\"urten, Roberto Rigolin F. Lopes, Veit Hagenmeyer
arXiv:2607. 01305v1 Announce Type: cross Abstract: Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things (IoT), enterprise, and distributed network environments.
By Jiefei Liu, Abu Saleh Md Tayeen, Pratyay Kumar, Qixu Gong, Wenbin Jiang, Huiping Cao, Satyajayant Misra, Jayashree Harikumar
The paper investigates how privacy guarantees, robustness to Byzantine attacks, and detection coverage for rare intrusion types interact in federated network intrusion detection systems. It introduces geometric indistinguishability to explain how privacy noise can obscure minority-class signals, and demonstrates through experiments on UNSW‑NB15 that combining differential privacy with robust aggregation can disproportionately harm detection of rare attacks. The study highlights that these properties cannot be treated as independently composable and calls for aggregation‑aware modeling and sample‑aware evaluation to build trustworthy federated NIDS.
By Adrita Rahman Tory, ABM Shawkat Ali, Md Abu Layek, Khondokar Fida Hasan
arXiv:2608. 11802v1 Announce Type: cross Abstract: Methods to increase the resilience of systems to cyber-attacks become increasingly important.
By Martin Sachenbacher, Martin Leucker, Alexander Weiss, Aliyu Tanko Ali
arXiv:2504. 01882v2 Announce Type: replace Abstract: The use of DNS over HTTPS (DoH) tunneling by an attacker to hide malicious activity within encrypted DNS traffic poses a serious threat to network security, as it allows malicious actors to bypass traditional monitoring and intrusion detection systems while evading detection by conventional traffic analysis techniques.
By Diego Cajaraville-Aboy, Marta Moure-Garrido, Carlos Beis-Penedo, Carlos Garcia-Rubio, Rebeca P. D\'iaz-Redondo, Celeste Campo, Ana Fern\'andez-Vilas, Manuel Fern\'andez-Veiga
arXiv:2503. 00065v4 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) achieve high performance in various real-world applications, such as drug discovery, traffic states prediction, and recommendation systems.
By Jing Xu, Franziska Boenisch, Adam Dziedzic