Enhancing Security for NFV-Based IOT Networks through Machine Learning: A Comprehensive Review and Analysis

Abstract

The past several years have seen a notable surge in the adoption of Internet of Things devices, leading to the development of NFV-based IoT networks. However, security remains a significant concern in these networks, as they are vulnerable to various attacks. Machine learning is a promising solution for enhancing security in NFV-based IoT networks. This paper presents a comprehensive review and analysis of the latest research on security enhancement for NFV-based IoT networks using machine learning. The paper aims to identify the current state-of-the-art techniques used to enhance security in these networks and highlight the potential benefits of machine learning in this context. The integration of machine learning in NFV-based IoT networks can significantly enhance security against emerging threats. The absence of standards, affordable and efficient machine learning systems is only a few of the issues that must be resolved. One critical aspect is the development of effective machine learning systems capable of identifying malicious traffic and handling the vast attack surface and different attack vectors. In order to achieve this, the study examines how SDN and NFV are being adopted to protect IoT networks from new risks.  This area of research holds great potential for improving the accuracy and efficiency of anomaly detection in NFV networks, keeping pace with rapidly evolving threats. In this regard, the paper provides a survey of machine learning-based algorithms for intrusion detection in NFV networks for enhancing security.