A Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detection

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dc.contributor.authorAmin, Reham
dc.contributor.authorEl-Taweel, Ghada
dc.contributor.authorAli, Ahmed Fouad
dc.contributor.authorTahoun, Mohamed
dc.coverage.issue2cs
dc.coverage.volume29cs
dc.date.accessioned2024-01-11T09:48:07Z
dc.date.available2024-01-11T09:48:07Z
dc.date.issued2023-12-31cs
dc.description.abstractThe vast amounts of data, lack of scalability, and low detection rates of traditional intrusion detection technologies make it impossible to keep up with evolving and increasingly sophisticated cyber threats. Therefore, there is an urgent need to detect and stop cyber threats early. Deep Learning has greatly improved intrusion detection due to its ability to self-learn and extract highly accurate features. In this paper, a Hybrid XG Boosted and Long Short-Term Memory algorithm (HXGBLSTM) is proposed. A comparative analysis is conducted between the computational performance of six established evolutionary computation algorithms and the recently developed bio-inspired metaheuristic algorithm called Zebra Optimisation Algorithm. These algorithms include the Particle Swarm Optimisation Algorithm, the Bio-inspired Algorithms, Bat Optimisation Algorithm, Firefly Optimisation Algorithm, and Monarch Butterfly Optimisation Algorithm, as well as the Genetic Algorithm as an Evolutionary Algorithm. The dimensionality curse has been mitigated by using these metaheuristic methods for feature selection, and the results are compared with the wrapper-based feature selection XGBoost algorithm. The proposed algorithm uses the CSE-CIC -IDS2018 dataset, which contains the latest network attacks. XGBoost outperformed the other FS algorithms and was used as the feature selection algorithm. In evaluating the effectiveness of the newly proposed HXGBLSTM, binary and multi-class classifications are considered. When comparing the performance of the proposed HXGBLSTM for cyber threat detection, it outperforms seven innovative deep learning algorithms for binary classification and four of them for multi-class classification. Other evaluation criteria such as recall, F1 score, and precision have been also used for comparison. The results showed that the best accuracy for binary classification is 99.8\%, with F1-score of 99.83\%, precision of 99.85\%, and recall of 99.82\%, in extensive and detailed experiments conducted on a real dataset. The best accuracy, F1-score, precision, and recall for multi-class classification were all around 100\%, which does give the proposed algorithm an advantage over the compared ones.en
dc.formattextcs
dc.format.extent307-322cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2023 vol. 29, č. 2, s. 307-322. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2023.2.307en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttps://hdl.handle.net/11012/244258
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/288cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectCyber Securityen
dc.subjectIntrusion Detectionen
dc.subjectDeep Learningen
dc.subjectFeature Selectionen
dc.titleA Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detectionen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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Vol. 29, No. 2