Cargando…
A distributed SDN-based intrusion detection system for IoT using optimized forests
Along with the expansion of Internet of Things (IoT), the importance of security and intrusion detection in this network also increases, and the need for new and architecture-specific intrusion detection systems (IDS) is felt. In this article, a distributed intrusion detection system based on a soft...
Autor principal: | |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10468089/ https://www.ncbi.nlm.nih.gov/pubmed/37647336 http://dx.doi.org/10.1371/journal.pone.0290694 |
_version_ | 1785099170728640512 |
---|---|
author | Luo, Ke |
author_facet | Luo, Ke |
author_sort | Luo, Ke |
collection | PubMed |
description | Along with the expansion of Internet of Things (IoT), the importance of security and intrusion detection in this network also increases, and the need for new and architecture-specific intrusion detection systems (IDS) is felt. In this article, a distributed intrusion detection system based on a software defined networking (SDN) is presented. In this method, the network structure is divided into a set of sub-networks using the SDN architecture, and intrusion detection is performed in each sub-network using a controller node. In order to detect intrusion in each sub-network, a decision tree optimized by black hole optimization (BHO) algorithm is used. Thus, the decision tree deployed in each sub-network is pruned by BHO, and the split points in its decision nodes are also determined in such a way that the accuracy of each tree in detecting sub-network attacks is maximized. The performance of the proposed method is evaluated in a simulated environment and its performance in detecting attacks using the NSLKDD and NSW-NB15 databases is examined. The results show that the proposed method can identify attacks in the NSLKDD and NSW-NB15 databases with an accuracy of 99.2% and 97.2%, respectively, which indicates an increase compared to previous methods. |
format | Online Article Text |
id | pubmed-10468089 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-104680892023-08-31 A distributed SDN-based intrusion detection system for IoT using optimized forests Luo, Ke PLoS One Research Article Along with the expansion of Internet of Things (IoT), the importance of security and intrusion detection in this network also increases, and the need for new and architecture-specific intrusion detection systems (IDS) is felt. In this article, a distributed intrusion detection system based on a software defined networking (SDN) is presented. In this method, the network structure is divided into a set of sub-networks using the SDN architecture, and intrusion detection is performed in each sub-network using a controller node. In order to detect intrusion in each sub-network, a decision tree optimized by black hole optimization (BHO) algorithm is used. Thus, the decision tree deployed in each sub-network is pruned by BHO, and the split points in its decision nodes are also determined in such a way that the accuracy of each tree in detecting sub-network attacks is maximized. The performance of the proposed method is evaluated in a simulated environment and its performance in detecting attacks using the NSLKDD and NSW-NB15 databases is examined. The results show that the proposed method can identify attacks in the NSLKDD and NSW-NB15 databases with an accuracy of 99.2% and 97.2%, respectively, which indicates an increase compared to previous methods. Public Library of Science 2023-08-30 /pmc/articles/PMC10468089/ /pubmed/37647336 http://dx.doi.org/10.1371/journal.pone.0290694 Text en © 2023 Ke Luo https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Luo, Ke A distributed SDN-based intrusion detection system for IoT using optimized forests |
title | A distributed SDN-based intrusion detection system for IoT using optimized forests |
title_full | A distributed SDN-based intrusion detection system for IoT using optimized forests |
title_fullStr | A distributed SDN-based intrusion detection system for IoT using optimized forests |
title_full_unstemmed | A distributed SDN-based intrusion detection system for IoT using optimized forests |
title_short | A distributed SDN-based intrusion detection system for IoT using optimized forests |
title_sort | distributed sdn-based intrusion detection system for iot using optimized forests |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10468089/ https://www.ncbi.nlm.nih.gov/pubmed/37647336 http://dx.doi.org/10.1371/journal.pone.0290694 |
work_keys_str_mv | AT luoke adistributedsdnbasedintrusiondetectionsystemforiotusingoptimizedforests AT luoke distributedsdnbasedintrusiondetectionsystemforiotusingoptimizedforests |