FAST School of Computing, National University of Computer and Emerging Sciences, Karachi 75030, Pakistan.
College Education & Literacy Department, Khursheed Government Girls Degree College, Government of Sindh, Karachi 75230, Pakistan.
Sensors (Basel). 2023 Jun 5;23(11):5340. doi: 10.3390/s23115340.
Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.
网络功能虚拟化（NFV）是一项快速发展的技术，它能够实现传统网络硬件组件的虚拟化，带来降低成本、提高灵活性和有效利用资源等好处。此外，NFV 在传感器和物联网网络中发挥着关键作用，可确保资源的最优使用和有效的网络管理。然而，在这些网络中采用 NFV 也带来了必须及时有效解决的安全挑战。本调查论文专注于探索与 NFV 相关的安全挑战。它提出利用异常检测技术来减轻网络攻击的潜在风险。研究评估了各种基于机器学习的算法在检测 NFV 网络中基于网络的异常方面的优缺点。通过深入了解 NFV 网络中进行及时有效的异常检测最有效的算法，本研究旨在协助网络管理员和安全专业人员加强 NFV 部署的安全性，从而保护传感器和物联网系统的完整性和性能。
