Future sixth-generation (6G) wireless networks are envisioned as a backbone for mission-critical applications, including autonomous transportation, smart grids, space communications, and tactical defense systems. Ensuring continuous service delivery under adversarial conditions is a fundamental goal of Zero Trust Architectures (ZTA) for Future G. This paper presents an integrated framework that combines stochastic cyberattack modeling with cost-constrained survivability optimization to enhance resilience in 6G mission-critical networks. We extend the methodology to 6G radio access networks (RAN) and core infrastructures by building upon validated survivability models for cyber-physical systems (CPSs) and stochastic attack rate prediction. We also incorporate redundancy allocation strategies and series-parallel reliability modeling. Cyber-attacks are characterized by using stochastic processes such as Poisson and negative binomial distributions to predict attack rates and dynamically update trust scores within ZTA policy engines. The proposed approach is supported by comparative analysis, including numerical insights from prior validated studies and literature benchmarks. The framework aligns with 3GPP security enhancements for 6G and offers a pathway toward unbreakable resilience in Zero Trust-enabled mission-critical networks.
Radio astronomy (RA) studies the universe by detecting extremely weak radio signals using highly sensitive antennas. The increasing deployment of 5G base stations (BSs) in neighboring frequency bands creates a risk of out-of-band (OOB) interference, threatening to disrupt RA. To address this risk, one would have to impose a limit on 5G deployment, which could in turn adversely affect its area coverage. In this paper, we present Varuna—an algorithm designed to maximize 5G network coverage while ensuring the OOB interference threshold at passive RA site is satisfied. Varuna operates by selectively adjusting the transmit power of 5G BSs based on a penalty-driven metric that balances coverage loss against interference reduction. Through simulation experiments on a real-world Very Large Baseline Antenna (VLBA) site and using the Irregular Terrain Model (ITM) propagation model, we demonstrate that Varuna achieves nearly maximum spatial coverage while offering OOB interference protection for RA.
Satellites serve as a foundational element of modern society, supporting a wide range of critical functions across communication, navigation, environmental monitoring, and national security. The growing accessibility of low-cost software-defined radio (SDR) systems has heightened the vulnerability of legacy satellite systems, which often lack robust cryptographic protections, to spoofing and replay attacks. In this paper, we propose a radio fingerprinting system centered on an autoencoder-based deep learning model enhanced with an attention mechanism to capture structured and/or temporal patterns in satellite data and improve attack detection accuracy. The model was trained and evaluated on a dataset of 1.7 million satellite downlink messages, and its performance was benchmarked against a state-of-the-art satellite radio fingerprinting system. Experimental results show that our approach outperforms the state-of-the-art fingerprinting system by 13% in ROC Area Under Curve (AUC) and 13% in Equal Error Rate (EER).
In this paper we develop a novel disruption-resilient approach for real-time, high-resolution sensor data delivery over multiple wireless channels for military autonomous systems such as drones, autonomous vehicles and robots. We design two innovative neural multiple description codecs (neural MDCs) which compress and encode images into multiple independently decodable and mutually refineable streams. Our approach not only achieves high compression efficiency, but also enables the effective use of multiple diverse radio channels for real-time delivery of high-resolution sensor data while ensuring disruption resiliency. Using benchmark image/video sensor datasets as well as real-world 5G traces, we evaluate and demonstrate the efficacy of both neural MDC codecs for high-resolution sensor data streaming over multiple radio channels under various jamming scenarios.
Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the Open-RAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, can lead to performance degradation and instability in O-RAN if not identified efficiently. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs).In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden non-Euclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.
