The arrival of 5G era brings great convenience and unprecedented opportunities to human society under the new technical features of ultra-high speed, ultra-low latency, and mega-connectivity. As the management hub and "brain" of the 5G network architecture, the security of the core network is paramount. However, its evolution towards cloudification has led to frequent cybersecurity incidents; an attack on the 5G core network could trigger severe consequences. To address this, this paper first systematically analyzes potential vulnerabilities and attack paths within the 5G core network from an attacker’s perspective. Subsequently, an endogenous proactive defense architecture, grounded in the principles of dynamism, heterogeneity, and redundancy, is proposed. The working mechanism of this architecture is elaborated in detail, alongside a theoretical analysis of its security properties. To validate the efficacy of the proposed architecture, defensive performance was first compared across different schemes via simulation. The results demonstrate that our architecture achieves optimal defensive performance while significantly increasing the attacker’s cost. Furthermore, the proposed architecture was implemented on the Unified Data Management (UDM) network function within the open-source free5gc platform, and its enhanced protective capability was assessed through real-world attack testing. Experimental results indicate that the architecture effectively elevates the overall security posture of the 5G core network, introducing only marginal performance overhead.

As a key intelligent component of core networks, NWDAF (Network Data Analytics Function) provides network intelligence capabilities while facing increasingly data security challenges. Current research on NWDAF data security primarily adopts federated learning framework, which trains models locally to avoid privacy leakage risks. However, existing NWDAF federated learning studies mostly rely on idealized assumptions, failing to adequately address two critical issues in real-world network deployments: 1) uneven data distribution across different regions, and 2) differentiated optimization requirements for high priority security regions. To solve these problems, we propose a region dual-factor aware federated learning framework for NWDAF (NWDAF-FedRDA), specifically designed to handle regional data imbalance and security priority differences in core networks. The framework comprises three core components: 1) Model parameter distribution: enabling allocation of global model parameters to regional nodes; 2) Local training: performing regional data preprocessing and model training; 3) Region dual-factor aware aggregation: computing regional weights through awareness factors and executing global model aggregation. Experimental evaluations across four network scenarios demonstrate the framework’s performance in anomaly detection tasks. Results show that compared to baseline methods, our framework exhibits significant advantages in uneven data distribution environments while effectively enhancing model performance for high-priority security regions.

Enhancement of service function chain (SFC) security ability by composing virtual network functions (VNFs) and allocating resources considering their security attributes can address the vulnerability threats in cloud environments, which is an important means of attempting to secure SFCs at the deployment stage. However, existing works do not consider the vulnerability correlation of the multi-step attack chains when completing SFC deployment based on trustworthiness. This results in existing security orchestration methods ignoring the differences in trustworthiness among network entities and focusing only on local trust optimization; these steps effectively disrupt the attack chains to secure SFCs. In this article, an innovative hierarchical trust model is proposed to assess the differentiated trustworthiness among network entities caused by vulnerability correlation. On the basis of trustworthiness assessment, both virtual trust of VNF combinations at the SFC composition stage and physical trust of physical node (PN) selections at the SFC placement stage are globally considered to disrupt the attack chains in SFCs as much as possible. To this end, the security-aware and cost-efficient SFC composition and placement (SCSCP) problem is formulated as an integer linear programming (ILP) problem, which is NP-hard. To tackle the SCSCP problem, the joint trust and cost global optimization (JTCGO) algorithm is proposed to dynamically update the trustworthiness and globally find the SFC deployment solutions including the VNF combination schemes and PN selection schemes. Simulation results demonstrate that our proposed algorithm can provide the optimal SFC deployment solutions for requests and can guarantee the SFC trustworthiness at a controllable cost, thereby protecting SFCs from network attacks in complex security environments.

As containerization technology progresses, the network functions within the 5G core are abstracted into Virtual Network Functions (VNFs), operating within a cloud-based ecosystem. To bolster network resilience, autoscaling strategies dynamically allocate resources in response to workloads, ensuring the Quality of Service (QoS) is maintained. However, the inherent openness of 5G networks exposes them to signaling storms and unexpected disturbances, leading to substantial workload fluctuations and bursts. Current reactive autoscaling strategies are slow to respond to these challenges, falling short of user expectations for low-latency, high-quality internet services. To address this challenge, we propose a novel auto-scaling framework specifically designed for the 5G cloud core network system, dubbed 5G-PPDE. Our framework harnesses the Crossformer model for accurate workload forecasting and employs a Multi-Layer Perceptron (MLP) classification model to offer four distinct scaling strategies. The scaling-down decision algorithm implements a Gradual Decrease Strategy (GDS) for resource management, while the burst scaling strategy adapts to sudden workload changes by referencing the maximum service quantities observed during historical window periods. We have constructed a validation system leveraging the open-source Free5GC and Free5GMANO, using the wrk tool to simulate burst traffic scenarios. Prometheus is utilized for real-time data collection, thereby validating the efficacy of our proposed framework. The findings demonstrate that our method effectively mitigates the impact of burst traffic, outperforming current solutions.

The adoption of SDN/NFV opens avenues for efficient network slicing deployment and cost control. However, the dynamic cost reduction brought by deployment location optimization is not suitable for all scenarios. To further reduce the cost, we recommend a sharing strategy in NFV. In this paper, we introduce a two-layer VNF multiplexing architecture, named PSMA, which guarantees both efficient VNF sharing operations and secure slicing during multiplexing. Leveraging the SDN/NFV features, the proposed scheme splits data processing and key management and establish secure connections using SDN’s programmable routing. The provided framework integrates comprehensive life-cycle management and key delivery mechanisms. The article substantiates its availability through extensive simulations of VNF reuse on a randomly generated network with Virtual Network Requests (VNR). The empirical results indicate a significant cost reduction of 5% to 10%, particularly pronounced in scenarios involving a substantial number of short-lived and transitional VNFs.

Abstract This paper explores the potential application of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR‐RIS) in improving the physical layer security (PLS) of millimetre wave (mmWave) non‐orthogonal multiple access (NOMA) uplink communications. In particular, the legitimate users on both sides of STAR‐RIS send confidential information simultaneously to the base station (BS) while keeping it secret from the eavesdroppers near the BS by exploiting the STAR‐RIS to proactively regulate the electromagnetic propagation environment. By jointly designing the transmit power, the active beamforming at the BS, and the reflecting/transmitting coefficients at the STAR‐RIS, the authors' goal is to maximize the minimum secrecy capacity subject to the successive interference cancellation decoding order constraints. Due to the non‐convexity of the formulated problem, an efficient algorithm is proposed by capitalizing on alternating optimization, a penalty‐based approach, successive convex approximation, and semi‐definite relaxation. Firstly, a two‐layer iterative algorithm based on the penalty for the joint beamforming optimization sub‐problem is proposed. Then, the non‐convex problem is transformed into a convex positive semi‐definite programming problem for the transmit power sub‐problem. Numerical simulation results reveal that the STAR‐RIS‐aided system has more tremendous advantages and effectiveness in improving the PLS of mmWave NOMA uplink communication compared with the benchmark schemes.

Abstract The reconfigurable intelligent surface (RIS) fundamentally changes the existing anti‐jamming paradigm by reconstructing the wireless propagation environment. However, the existing RIS‐based anti‐jamming work mainly considers narrow‐band frequency‐flat fading channels. As an extension of existing work, the use of RIS to enhance the anti‐jamming capability of broadband orthogonal frequency division multiplexing (OFDM) system in frequency‐selective fading channels is first studied. In the multipath frequency‐selective fading channels, the anti‐jamming design needs to satisfy the channel gains of all signal paths with different delays. To this end, the authors propose a joint design scheme of active precoding at the transmitter and passive precoding at the RIS to attenuate the jamming signal and enhance the legitimate signal, and formulate the joint design objective to maximize the average rate of all subcarriers. An alternating optimization algorithm is developed to address this non‐convex problem due to the complex coupling of active and passive precoding. In detail, the active and passive precoding are solved by the Cauchy–Schwarz inequality and power water‐filling method and the alternating direction method of multipliers method assisted by multidimensional complex quadratic transform, respectively. Simulation results demonstrate that the proposed method can significantly improve the anti‐jamming communication performance of the OFDM system compared with the scheme without RIS. In addition, the impact of the number of RIS elements and deployment location on system performance is revealed.

Existing works on solving jamming signals within the receiving antenna's bandwidth are generally difficult to strike a balance between flexibility and effectiveness. To this end, we propose a joint space-frequency anti-jamming scheme. Considering the broadband response of the Reconfigurable Intelligent Surface (RIS), we innovatively exploit different spatial and frequency responses of the RIS to generate space-frequency filtering during the propagation of the jamming signal. We formulate a problem to design reflection coefficients of the RIS so the jamming signals will be attenuated, but the legitimate signal will be enhanced at the receiver. Then the problem is equiv-alently transformed into a quadratically constrained quadratic programming (QCQP) problem which can be efficiently solved. We also present a jamming suppression scheme when legitimate transmitter pilots are submerged. Simulation results show that the proposed joint space-frequency anti-jamming scheme consti-tutes an equivalent broadband band-stop filter to the jamming signal and has better resistance to strong jamming. Meanwhile, the anti-jamming ability will further increase with the number of RIS elements. As an equivalent space-frequency filter embedded in the wireless environment, the proposed scheme introduces a different filtering paradigm to collaborate with existing anti-jamming techniques effectively and reveal its potential in some military or civilian scenarios.

Abstract This paper investigates a three‐node proactive eavesdropping system with the aid of intelligent reflecting surface (IRS), where a monitor tries to wiretap information and transmit jamming signals simultaneously to interfere with the suspicious link. The IRS is deployed around the suspicious users to reconstruct propagation channel. Meanwhile, with large low‐cost passive reflecting elements, it can provide more spatial degrees of freedom to moderate the link quality. In order to degrade exposure risk of eavesdropping behavior, the authors' objective is to minimize the jamming power by carefully designing jamming beamforming vectors and the phase shifts under the constraint of reliable intercepted information. The formulated problem is non‐trivial to solve due to the coupling variables and unit‐modulus constraints. Fortunately, by using alternating optimization and successive convex approximation techniques, the original problem is transformed into convex form and a sub‐optimal solution is achieved. Numerical results show that the proposed scheme can effectively reduce the jamming power and the number of the jamming antennas over benchmark schemes, which is quite suitable for covertly overhearing signal.

In this paper, secure transmission in a simultaneous wireless information and power transfer technology-enabled heterogeneous network with the aid of multiple IRSs is investigated. As a potential technology for 6G, intelligent reflecting surface (IRS) brings more spatial degrees of freedom to enhance physical layer security. Our goal is to maximize the secrecy rate by carefully designing the transmit beamforming vector, artificial noise vector, and reflecting coefficients under the constraint of quality-of-service. The formulated problem is hard to solve due to the nonconcave objective function as well as the coupling variables and unit-modulus constraints. Fortunately, by using alternating optimization, successive convex approximation, and sequential Rank-1 constraint relaxation approach, the original problem is transformed into convex form and a suboptimal solution is achieved. Numerical results show that the proposed scheme outperforms other existing benchmark schemes without IRS and can maintain promising security performance as the number of terminals increases with lower energy consumption.