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In the rapidly evolving landscape of 5G technology, safeguarding Radio Frequency (RF) environments against sophisticated intrusions is paramount, especially in dynamic spectrum access and management. This paper presents an enhanced experimental model that integrates a self-attention mechanism with a Recurrent Neural Network (RNN)-based autoencoder for the detection of anomalous spectral activities in 5G networks at the waveform level. Our approach, grounded in time-series analysis, processes in-phase and quadrature (I/Q) samples to identify irregularities that could indicate potential jamming attacks. The model's architecture, augmented with a self-attention layer, extends the capabilities of RNN autoen-coders, enabling a more nuanced understanding of temporal dependencies and contextual relationships within the RF spectrum. Utilizing a simulated 5G Radio Access Network (RAN) test-bed constructed with srsRAN 5G and Software Defined Radios (SDRs), we generated a comprehensive stream of data that reflects real-world RF spectrum conditions and attack scenarios. The model is trained to reconstruct standard signal behavior, establishing a normative baseline against which deviations, indicative of security threats, are identified. The proposed architecture is designed to balance between detection precision and computational efficiency, so the LSTM network, enriched with self-attention, continues to optimize for minimal execution latency and power consumption. Conducted on a real-world SDR-based testbed, our results demonstrate the model's improved performance and accuracy in threat detection. 

The open radio access network (O-RAN) offers new degrees of freedom for building and operating advanced cellular networks. Emphasizing on RAN disaggregation, open interfaces, multi-vendor support, and RAN intelligent controllers (RICs), O-RAN facilitates adaptation to new applications and technology trends. Yet, this architecture introduces new security challenges. This article proposes leveraging zero trust principles for O-RAN security. We introduce zero trust RAN (ZTRAN), which embeds service authentication, intrusion detection, and secure slicing subsystems that are encapsulated as xApps. We implement ZTRAN on the open artificial intelligence cellular (OAIC) research platform and demonstrate its feasibility and effectiveness in terms of legitimate user throughput and latency figures. Our experimental analysis illustrates how ZTRAN's intrusion detection and secure slicing microservices operate effectively and in concert as part of O-RAN Alliance's containerized near-real time RIC. Research directions include exploring machine learning and additional threat intelligence feeds for improving the performance and extending the scope of ZTRAN. 

The evolution of open architectures for Radio Ac-cess Networks (RANs) is revolutionizing network management and optimization. This transformation, fostered by O-RAN, expedites data acquisition and examination by exploiting newly established open interfaces. Moreover, it has led to the rise of near real-time RAN Intelligent Controllers (RICs), instigating a wave of AI-driven applications, or xApps, that employ Artificial Intelligence (AI)/Machine Learning (ML) methods. Nevertheless, deploying xApps as centralized applications presents substantial challenges, such as handling vast data transactions, potential delays, and security vulnerabilities, which are notably prominent within the multifaceted, decentralized, multivendor, and trustless nature of open networks. To alleviate these predicaments, a transition from centralized apps operating in near real-time to distributed real-time apps is imperative for augmented security and efficiency. This paper addresses these complexities by introducing an open platform that integrates a federated reinforcement learning algorithm to operate as distributed Apps (dApps) within the next-generation O-RAN architecture. We present evaluation results in a specific test environment. 

The open radio access network (O-RAN) is recognized for its modularity and adaptability, facilitating swift responses to emerging applications and technological advancements. However, this architecture's disaggregated nature, coupled with support from various vendors, introduces new security challenges. This paper proposes an innovative approach to bolster the security of future O-RAN deployments by leveraging RAN slicing principles. Central to this security enhancement is the concept of secure slicing. We introduce SliceX, an xApp designed to safeguard RAN resources while ensuring strict throughput and latency requirements are met for legitimate users. Leveraging the open artificial intelligence cellular re-search (OAIC) platform, we observed that the network latency averages around ten microseconds in a default configuration without SliceX. The latency escalates to over seven seconds in the presence of a malicious user equipment (UE) flooding the net-work with requests. SliceX intervenes, restoring network latency to normal levels, with a maximum latency of approximately 2.3 s. These and other numerical findings presented in this paper affirm the tangible advantages of SliceX in mitigating security threats and ensuring that 0- RAN deployments meet stringent performance requirements. Our research demonstrates the real-world effectiveness of secure slicing, making SliceX a valuable tool for military, government, and critical infrastructure opera-tors reliant on public wireless communication networks to fulfill their security, resiliency, and performance objectives. 

The open radio access network (O-RAN) describes an industry-driven open architecture and interfaces for building next generation RANs with artificial intelligence (AI) controllers. We circulated a survey among researchers, developers, and practitioners to gather their perspectives on O-RAN as a framework for 6G wireless research and development (R&D). The majority responded in favor of O-RAN and identified R&D of interest to them. Motivated by these responses, this paper identifies the limitations of the current O-RAN specifications and the technologies for overcoming them. We recognize end-to-end security, deterministic latency, physical layer real-time control, and testing of AI-based RAN control applications as the critical features to enable and discuss R&D opportunities for extending the architectural capabilities of O-RAN as a platform for 6G wireless.