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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 advent of 5G technology introduces significant advancements in speed, latency, and device connectivity, but also poses complex security challenges. Among typical denial-of-service (DoS) attacks, jamming attack can severely degrade network performance by interfering critical communication channels. To address this issue, we propose a novel security solution utilizing multipath communication, which distributes message segments across multiple paths to ensure message recovery even when some paths are compromised. This strategy enhances network resilience and aligns with zero-trust architecture principles. Moreover, the proposed scheme has been implemented in our testbed to validate the concept and assess the network performance under jamming attacks. Our findings demonstrate that this method eliminates the negative impacts caused by DoS attacks, maintaining the integrity and availability of critical network services. The results highlight the robustness of multipath communication in securing 5G networks against sophisticated attacks, thereby safeguarding essential communications in dynamic and potentially hostile environments. 

The open radio access network (O-RAN) represents a paradigm shift in RAN architecture, integrating intelligence into communication networks via xApps -- control applications for managing RAN resources. This integration facilitates the adoption of AI for network optimization and resource management. However, there is a notable gap in practical network performance analyzers capable of assessing the functionality and efficiency of xApps in near real-time within operational networks. Addressing this gap, this article introduces a comprehensive network performance analyzer, tailored for the near-real time RAN intelligent controller. We present the design, development, and application scenarios for this testing framework, including its components, software, and tools, providing an end-to-end solution for evaluating the performance of xApps in O-RAN environments. 

This demonstration explores the security concerns in 5G and beyond networks within open radio access network (O-RAN) deployments, focusing on active attacks disrupting cellular communications. An xApp developed on the open artificial intelligence cellular (OAIC) platform enables on-the-fly creation and management of network slices to mitigate such attacks. The xApp is hosted in the near-real time RAN intelligent controller (RIC) and establishes secure slices for the software radio network it controls. This solution presents a practical approach for resilient and secure network management in dynamic environments.