The paper introduces SAFA-MZ, a secure adaptive federated authentication scheme for multi‑zone non‑terrestrial networks that embeds group‑level authentication tags into a collaborative rate‑splitting multiple access transmission. It jointly optimizes high‑altitude platform station placement, user association, and power allocation to maximize secrecy spectral efficiency while meeting authentication reliability, power, and coverage constraints. The solution employs a repair‑based cross‑entropy method and a graph‑aware advantage actor‑critic algorithm, achieving up to 135% higher average secrecy spectral efficiency compared to single‑connect transmission.
By Parsa Rajabi, Mohammad Mirzaee, Mohammad Reza Abedi, Nader Mokari, Paeiz Azmi
arXiv:2607. 04698v1 Announce Type: new Abstract: 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).
By Mohammad Ansarimehr, Somayeh Changiz, Ehsan Baghishani, Ali Mousavi
The paper introduces SEAL, a training-time, parameter‑efficient defense that attaches a plug‑and‑play adapter to the shared expert component of Mixture‑of‑Experts models, and SEAL++, which adds an orthogonal constraint to preserve existing safety subspaces. By leveraging the always‑activated shared expert, SEAL mitigates the structural vulnerability of sparse routing to adversarial manipulation, reducing attack success rates by up to 60% with minimal impact on model capability. The approach is evaluated across six attack scenarios involving harmful prompting, jailbreaks, malicious fine‑tuning, and neuron pruning.
By Qingyu Meng, Yiwei Zha, Jiahuan Pei, Koen Hindriks, Herbert Bos, Min Chen
arXiv:2609.00284v1 Announce Type: cross
Abstract: Emerging 6G wireless networks are expected to operate across diverse deployment scenarios, where variations in network topology, user mobility, traff...
By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
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. 15469v1 Announce Type: cross Abstract: Federated Learning (FL) over 5G cellular networks protects raw data but remains vulnerable to side-channel leakage.
By Md Nahid Hasan Shuvo, Mahmudul Hassan Ashik, Moinul Hossain
Federated Learning (FL) over 5G cellular networks protects raw data but remains vulnerable to side-channel leakage. Prior fingerprinting attacks assume packet-level network visibility, an assumption that does not hold at the 5G Physical (PHY) layer, where user payloads are encrypted and Radio Network Temporary Identifiers (RNTIs) may change over time.
arXiv:2607. 06109v1 Announce Type: cross Abstract: Multi-perturbation adversarial training (MAT) aims to achieve robustness against multiple $\ell_p$ perturbations but suffers from robustness trade-offs between different threats.
By Woo Jae Kim, Kyle Min, Suhyeon Ha, Joonsung Jeon, Sung-eui Yoon
Diff‑DDoS is a three‑phase framework that uses tabular diffusion models to generate realistic cyber‑physical attacks and strengthen DDoS detectors for 5G‑enabled systems. First, a CNN cell‑level detector is trained on call detail record (CDR) grids; second, a tabular denoising diffusion probabilistic model (TabDDPM) learns normal CDR aggregates to synthesize realistic attacks; third, adversarial diffusion training (ADT) iteratively produces hard, distribution‑preserving samples that harden the detector. On the Milano CDR dataset, ResNet50 with ADT achieves near‑perfect F1‑scores across multiple attack scenarios, outperforming existing synthetic‑data methods such as CTGAN.
By Bilal Hussain, Xiao Tang, Qinghe Du, Tan Li, Muhammad Azhar, Danista Khan
Model merging composes specialized capabilities into a single LLM by aggregating task vectors sourced from unverified public platforms, exposing a critical supply-chain attack surface: Because any malicious behavior can be encoded into a task vector, and merging grants third-party vectors direct write access to model weights, an attacker-provided task vector can enable or amplify diverse downstream threats. Prior work studies only backdoor attacks against model merging for classifiers using static arithmetic heuristics, which fail to effectively handle diverse attacks on generative LLMs for three reasons.
arXiv:2510. 09288v2 Announce Type: replace-cross Abstract: The vulnerability of machine learning models to adversarial attacks remains a critical societal security challenge.
By Pablo G. Arce, Roi Naveiro, David R\'ios Insua
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