Emerging Vulnerabilities in Next-Generation Network Infrastructures: Threat Mitigation Frameworks for 5G-Edge-IoT Convergence Environments
DOI:
https://doi.org/10.53469/wjimt.2025.08(07).07Keywords:
Emerging Vulnerabilities, Threat Mitigation Frameworks, Data Transmission Protocols, Blockchain-Based Trust ManagementAbstract
The rapid convergence of 5G, edge computing, and the Internet of Things (IoT) is reshaping next-generation network infrastructures, enabling unprecedented levels of connectivity, low latency, and real-time data processing. However, this integration introduces a complex landscape of emerging security vulnerabilities that pose significant threats to the reliability, privacy, and integrity of critical systems. Traditional security frameworks, designed for isolated networks, are inadequate in addressing the dynamic and heterogeneous nature of 5G-edge-IoT convergence environments. This paper provides a comprehensive analysis of the emerging vulnerabilities inherent in these integrated systems, focusing on three key dimensions: network architecture, device-level security, and data transmission protocols. Firstly, we explore the architectural vulnerabilities arising from the decentralized and distributed nature of edge computing, which expands the attack surface by introducing numerous edge nodes susceptible to exploitation. Secondly, we examine device-level security challenges, including the proliferation of low-cost, resource-constrained IoT devices with limited built-in security mechanisms, making them prime targets for cyberattacks. Thirdly, we analyze vulnerabilities in data transmission protocols, particularly those related to the high-speed, low-latency requirements of 5G networks, which may compromise data confidentiality and integrity during transit. To mitigate these threats, we propose a multi-layered threat mitigation framework that integrates proactive and reactive security measures. The framework incorporates advanced encryption techniques, anomaly detection algorithms, and secure boot mechanisms to enhance device-level security. Additionally, it leverages software-defined networking (SDN) and network function virtualization (NFV) to dynamically adapt security policies based on real-time threat intelligence. Furthermore, we introduce a blockchain-based trust management system to ensure the integrity and non-repudiation of data transactions across the 5G-edge-IoT ecosystem. Through extensive simulations and case studies, we demonstrate the effectiveness of the proposed framework in significantly reducing the attack surface and improving the overall security posture of next-generation network infrastructures. Our findings underscore the importance of adopting a holistic and adaptive security approach to safeguard the convergence of 5G, edge computing, and IoT against evolving cyber threats.
References
Ding, C.; Wu, C. Self-Supervised Learning for Biomedical Signal Processing: A Systematic Review on ECG and PPG Signals. medRxiv 2024.
Zhang, Yuhan. "InfraMLForge: Developer Tooling for Rapid LLM Development and Scalable Deployment." (2025).
Hu, Xiao. "GenPlayAds: Procedural Playable 3D Ad Creation via Generative Model." (2025).
Qin, Haoshen, et al. "Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression." Digital health 11 (2025): 20552076251349719.
Li, X., Lin, Y., & Zhang, Y. (2025). A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy. arXiv preprint arXiv:2507.12098.
Li, X., Wang, X., & Lin, Y. (2025). Graph Neural Network Enhanced Sequential Recommendation Method for Cross-Platform Ad Campaign. arXiv preprint arXiv:2507.08959.
Zheng, Haoran, et al. "FinGPT-Agent: An Advanced Framework for Multimodal Research Report Generation with Task-Adaptive Optimization and Hierarchical Attention." (2025).
Chen, Yang, et al. "SyntheClean: Enhancing Large-Scale Multimodal Models via Adaptive Data Synthesis and Cleaning." 2025 5th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA). IEEE, 2025.
Jiang, Gaozhe, et al. "A Knowledge-Enhanced Multi-Task Learning Model for Domain-Specific Question Answering." 2025 7th International Conference on Information Science, Electrical and Automation Engineering (ISEAE). IEEE, 2025.
Zhuo, Jiayang, et al. "An Intelligent-Aware Transformer with Domain Adaptation and Contextual Reasoning for Question Answering." 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). IEEE, 2025.
Zhang, Hanlu, et al. "Dynamic Attention-Guided Video Generation from Text with Multi-Scale Synthesis and LoRA Optimization." 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT). IEEE, 2025.
Shih, Kowei, et al. "DST-GFN: A Dual-Stage Transformer Network with Gated Fusion for Pairwise User Preference Prediction in Dialogue Systems." 2025 8th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE, 2025.
Chen, Rensi. "The application of data mining in data analysis." International Conference on Mathematics, Modeling, and Computer Science (MMCS2022). Vol. 12625. SPIE, 2023.
Chen, Yinda, et al. "Generative text-guided 3d vision-language pretraining for unified medical image segmentation." arXiv preprint arXiv:2306.04811 (2023).
Sun, N., Yu, Z., Jiang, N., & Wang, Y. (2025). Construction of Automated Machine Learning (AutoML) Framework Based on Large LanguageModels.