Towards Security Enhancement for NFV-Based IoT Networks Using Machine Learning

DOI

10.2991/978-94-6463-252-1_40How to use a DOI?

Keywords

Intrusion Detection System (IDS); Network Function Virtualization (NFV); BoT-IoT; SEIR Model; Denial of Service (DoS)

Abstract

IoT networks are increasing day-to-day life and due to its growing use their sizes are increasing continuously. As IoT network contains different devices supporting different platforms these networks are vulnerable to different types of attacks. There is a need for an intrusion detection system that can recognize these attacks and carry out appropriate defenses against them in order to secure the network. Several other systems with individual classifiers have been tried, but they are insufficient to recognize attacks correctly and carry out defenses against them. This system introduces an ensemble classifier that integrates different classifiers and optimizes their parameters with Harris Hyena optimization. This optimization will improve classification accuracy and the rate of detection will be fast which helps to execute proper countermeasures against a particular attack In this work, these challenges are considered, and planning to introduce an efficient artificial intelligence (AI) approach to address the three key IoT security-related issues. Along with the AI approach, the NFV (Network function virtualization) will be integrated to develop a generalized Intrusion Detection System (IDS). Malware and virus present in the network are detected by this architecture. Lastly, the NFV surveillance zone patch structure-based IoT network’s scalability is assessed. This procedure aims to create an algorithm that can manage all IoT devices that are currently operating. The basic objective of this method is to develop a hybrid machine-learning algorithm that can support most, if not all, of the IoT devices, now being utilized for diverse purposes. This model will forecast malware assaults for NFV so that suitable defenses can be put in place. Data Controller distributes updates through its distance-bound service as soon as the malware virus is discovered in a particular base station database. An NFV service-based framework that is distance-bound allows for effective update distribution. The system handles a wide variety of adware and viruses that can be found. It provides NFV with a hybrid machine-learning algorithm and patching system to prevent malware spread in IoT networks.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sandeep N. Gite
AU  - Smita L. Kasar
PY  - 2023
DA  - 2023/11/09
TI  - Towards Security Enhancement for NFV-Based IoT Networks Using Machine Learning
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 361
EP  - 369
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_40
DO  - 10.2991/978-94-6463-252-1_40
ID  - Gite2023
ER  -