A novel deep learning-based framework with particle swarm optimisation for intrusion detection in computer networks.
Intrusion detection plays a significant role in the provision of information security. The most critical element is the ability to precisely identify different types of intrusions into the network. However, the detection of intrusions poses a important challenge, as many new types of intrusion are n...
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| Format: | Article |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316253 |
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| author | Abdullah Asım Yılmaz |
| author_facet | Abdullah Asım Yılmaz |
| author_sort | Abdullah Asım Yılmaz |
| collection | DOAJ |
| description | Intrusion detection plays a significant role in the provision of information security. The most critical element is the ability to precisely identify different types of intrusions into the network. However, the detection of intrusions poses a important challenge, as many new types of intrusion are now generated by cyber-attackers every day. A robust system is still elusive, despite the various strategies that have been proposed in recent years. Hence, a novel deep-learning-based architecture for detecting intrusions into a computer network is proposed in this paper. The aim is to construct a hybrid system that enhances the efficiency and accuracy of intrusion detection. The main contribution of our work is a novel deep learning-based hybrid architecture in which PSO is used for hyperparameter optimisation and three well-known pre-trained network models are combined in an optimised way. The suggested method involves six key stages: data gathering, pre-processing, deep neural network (DNN) architecture design, optimisation of hyperparameters, training, and evaluation of the trained DNN. To verify the superiority of the suggested method over alternative state-of-the-art schemes, it was evaluated on the KDDCUP'99, NSL-KDD and UNSW-NB15 datasets. Our empirical findings show that the proposed model successfully and correctly classifies different types of attacks with 82.44%, 90.42% and 93.55% accuracy values obtained on UNSW-B15, NSL-KDD and KDDCUP'99 datasets, respectively, and outperforms alternative schemes in the literature. |
| format | Article |
| id | doaj-art-b63da56b65134240aa1aaa4506b99d38 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-b63da56b65134240aa1aaa4506b99d382025-08-20T02:16:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031625310.1371/journal.pone.0316253A novel deep learning-based framework with particle swarm optimisation for intrusion detection in computer networks.Abdullah Asım YılmazIntrusion detection plays a significant role in the provision of information security. The most critical element is the ability to precisely identify different types of intrusions into the network. However, the detection of intrusions poses a important challenge, as many new types of intrusion are now generated by cyber-attackers every day. A robust system is still elusive, despite the various strategies that have been proposed in recent years. Hence, a novel deep-learning-based architecture for detecting intrusions into a computer network is proposed in this paper. The aim is to construct a hybrid system that enhances the efficiency and accuracy of intrusion detection. The main contribution of our work is a novel deep learning-based hybrid architecture in which PSO is used for hyperparameter optimisation and three well-known pre-trained network models are combined in an optimised way. The suggested method involves six key stages: data gathering, pre-processing, deep neural network (DNN) architecture design, optimisation of hyperparameters, training, and evaluation of the trained DNN. To verify the superiority of the suggested method over alternative state-of-the-art schemes, it was evaluated on the KDDCUP'99, NSL-KDD and UNSW-NB15 datasets. Our empirical findings show that the proposed model successfully and correctly classifies different types of attacks with 82.44%, 90.42% and 93.55% accuracy values obtained on UNSW-B15, NSL-KDD and KDDCUP'99 datasets, respectively, and outperforms alternative schemes in the literature.https://doi.org/10.1371/journal.pone.0316253 |
| spellingShingle | Abdullah Asım Yılmaz A novel deep learning-based framework with particle swarm optimisation for intrusion detection in computer networks. PLoS ONE |
| title | A novel deep learning-based framework with particle swarm optimisation for intrusion detection in computer networks. |
| title_full | A novel deep learning-based framework with particle swarm optimisation for intrusion detection in computer networks. |
| title_fullStr | A novel deep learning-based framework with particle swarm optimisation for intrusion detection in computer networks. |
| title_full_unstemmed | A novel deep learning-based framework with particle swarm optimisation for intrusion detection in computer networks. |
| title_short | A novel deep learning-based framework with particle swarm optimisation for intrusion detection in computer networks. |
| title_sort | novel deep learning based framework with particle swarm optimisation for intrusion detection in computer networks |
| url | https://doi.org/10.1371/journal.pone.0316253 |
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