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The Open Radio Access Network (O-RAN) architecture is reshaping telecommunications by promoting openness, flexibility, and intelligent closed-loop optimization. By decoupling hardware and software and enabling multi-vendor deployments, O-RAN reduces costs, enhances performance, and allows rapid adaptation to new technologies. A key innovation is intelligent network slicing, which partitions networks into isolated slices tailored for specific use cases or quality of service requirements. The RAN Intelligent Controller further optimizes resource allocation, ensuring efficient utilization and improved service quality for user equipment (UEs). However, the modular and dynamic nature of O-RAN expands the threat surface, necessitating advanced security measures to maintain network integrity, confidentiality, and availability. Intrusion detection systems have become essential for identifying and mitigating attacks. This research explores using large language models (LLMs) to generate security recommendations based on the temporal traffic patterns of connected UEs. The paper introduces an LLM-driven intrusion detection framework and demonstrates its efficacy through experimental deployments, comparing non-fine-tuned and fine-tuned models for task-specific accuracy. 

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. 

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. 

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. 

Abstract Genetic code expansion technology allows for the use of noncanonical amino acids (ncAAs) to create semisynthetic organisms for both biochemical and biomedical applications. However, exogenous feeding of chemically synthesized ncAAs at high concentrations is required to compensate for the inefficient cellular uptake and incorporation of these components into proteins, especially in the case of eukaryotic cells and multicellular organisms. To generate organisms capable of autonomously biosynthesizing an ncAA and incorporating it into proteins, we have engineered a metabolic pathway for the synthesis ofO‐methyltyrosine (OMeY). Specifically, we endowed organisms with a marformycins biosynthetic pathway‐derived methyltransferase that efficiently converts tyrosine to OMeY in the presence of the co‐factorS‐adenosylmethionine. The resulting cells can produce and site‐specifically incorporate OMeY into proteins at much higher levels than cells exogenously fed OMeY. To understand the structural basis for the substrate selectivity of the transferase, we solved the X‐ray crystal structures of the ligand‐free and tyrosine‐bound enzymes. Most importantly, we have extended this OMeY biosynthetic system to both mammalian cells and the zebrafish model to enhance the utility of genetic code expansion. The creation of autonomous eukaryotes using a 21st amino acid will make genetic code expansion technology more applicable to multicellular organisms, providing valuable vertebrate models for biological and biomedical research. 

Neural crest cells (NCCs) are vertebrate stem cells that give rise to various cell types throughout the developing body in early life. Here, we utilized single-cell transcriptomic analyses to delineate NCC-derivatives along the posterior developing vertebrate, zebrafish, during the late embryonic to early larval stage, a period when NCCs are actively differentiating into distinct cellular lineages. We identified several major NCC/NCC-derived cell-types including mesenchyme, neural crest, neural, neuronal, glial, and pigment, from which we resolved over three dozen cellular subtypes. We dissected gene expression signatures of pigment progenitors delineating into chromatophore lineages, mesenchyme cells, and enteric NCCs transforming into enteric neurons. Global analysis of NCC derivatives revealed they were demarcated by combinatorial hox gene codes, with distinct profiles within neuronal cells. From these analyses, we present a comprehensive cell-type atlas that can be utilized as a valuable resource for further mechanistic and evolutionary investigations of NCC differentiation.