A Novel Deep Learning-Based Framework With Particle Swarm Optimisation for Intrusion Detection in Computer Networks

dc.authorid Yilmaz, Abdullah Asim/0000-0002-3014-609X
dc.authorscopusid 57210803267
dc.authorwosid Yilmaz, Abdullah Asim/Aai-1622-2020
dc.contributor.author Yilmaz, Abdullah Asim
dc.date.accessioned 2025-03-05T20:47:02Z
dc.date.available 2025-03-05T20:47:02Z
dc.date.issued 2025
dc.department Atılım University en_US
dc.department-temp [Yilmaz, Abdullah Asim] Atilim Univ, Comp Engn Dept, Ankara, Turkiye en_US
dc.description Yilmaz, Abdullah Asim/0000-0002-3014-609X en_US
dc.description.abstract 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. en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.doi 10.1371/journal.pone.0316253
dc.identifier.issn 1932-6203
dc.identifier.issue 2 en_US
dc.identifier.pmid 39937819
dc.identifier.scopus 2-s2.0-85218171284
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1371/journal.pone.0316253
dc.identifier.uri https://hdl.handle.net/20.500.14411/10465
dc.identifier.volume 20 en_US
dc.identifier.wos WOS:001422038700099
dc.identifier.wosquality Q2
dc.institutionauthor Yilmaz, Abdullah Asim
dc.language.iso en en_US
dc.publisher Public Library Science en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 1
dc.title A Novel Deep Learning-Based Framework With Particle Swarm Optimisation for Intrusion Detection in Computer Networks en_US
dc.type Article en_US
dc.wos.citedbyCount 1
dspace.entity.type Publication

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