arXiv:2507. 17188v2 Announce Type: replace-cross Abstract: This paper investigates secure communications in rate-splitting multiple access (RSMA) enabled heterogeneous UAV networks, where multiple UAVs collaboratively serve ground terminals in the presence of eavesdroppers.
By Lijie Zheng, Ji He, Shih Yu Chang, Yulong Shen
The paper introduces SAFA-MZ, a secure adaptive framework for physical‑layer authentication in non‑terrestrial networks. It fuses multiple spatial, angular, combiner, subspace, and Doppler‑delay features into a distributed fingerprint and employs a causal meta‑learning strategy with invariant risk minimization to adapt quickly to new environments. A two‑stage authentication process combines local recognition with selective TDOA localization using a graph attention network, reducing backhaul overhead while achieving 92% accuracy and 96% AUC in simulations.
By Parsa Rajabi, Mohammad Reza Abedi, Nader Mokari, Paeiz Azmi, Halim Yanikomeroglu
arXiv:2609.26598v1 Announce Type: cross
Abstract: The future 6G networks are expected to incorporate a proliferation of wireless services in diverse environments, which presents a significant challen...
By Xiao Tang, Tong Hui, Chao Shen, Yichen Wang, Qinghe Du, Li Sun, Zhu Han
The paper investigates how federated learning (FL) updates in vehicular edge networks can reveal client identities through gradient-based attacks on inertial sensor data, using the UCI Human Activity Recognition benchmark as a proxy. Experiments show that an honest-but-curious server can identify clients with near-perfect accuracy from unprotected updates. The authors evaluate lightweight defenses—clipping followed by Gaussian noise and ensemble FL—to mitigate this privacy risk while preserving model utility, reporting differential‑privacy budgets and empirical results across multiple attack classifiers and data partitions.
By Ali Akarma (Islamic University of Madinah, Madinah, Saudi Arabia, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia), Toqeer Ali Syed (Islamic University of Madinah, Madinah, Saudi Arabia), Muhammad Khan (University of the West of England, Bristol, U.K), Qurat-ul-ain Mastoi (University of the West of England, Bristol, U.K), Adeel Ahmad (Islamic University of Madinah, Madinah, Saudi Arabia)
The rapid proliferation of Internet of things (IoT) devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems (IDS). However, centralized learning approaches often suffer from severe performance degradation due to high-dimensional traffic data, extreme class imbalance, and highly non-independent and identically distributed (non-IID) data across heterogeneous edge devices.
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