Application of BukaGini algorithm for enhanced feature İnteraction analysis in intrusion detection systems

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dc.authorscopusidCengiz Korhan / 56522820200
dc.authorscopusidSedat Akleylek / 15833929800
dc.authorwosidCengiz Korhan / ABD-5559-2020
dc.authorwosidSedat Akleylek / N-2620-2019
dc.contributor.authorBouke, Mohamed Aly
dc.contributor.authorAbdullah, Azizol
dc.contributor.authorCengiz, Korhan
dc.contributor.authorAkleylek, Sedat
dc.date.accessioned2025-04-17T13:16:22Z
dc.date.available2025-04-17T13:16:22Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThis article presents an evaluation of BukaGini, a stability-aware Gini index feature selection algorithm designed to enhance model performance in machine learning applications. Specifically, the study focuses on assessing BukaGini's effectiveness within the domain of intrusion detection systems (IDS). Recognizing the need for improved feature interaction analysis methodologies in IDS, this research aims to investigate the performance of BukaGini in this context. BukaGini's performance is evaluated across four diverse datasets commonly used in IDS research: NSLKDD (22,544 samples), WUSTL EHMS (16,318 samples), WSN-DS (374,661 samples), and UNSWNB15 (175,341 samples), amounting to a total of 588,864 data samples. The evaluation encompasses key metrics such as stability score, accuracy, F1-score, recall, precision, and ROC AUC. Results indicate significant advancements in IDS performance, with BukaGini achieving remarkable accuracy rates of up to 99% and stability scores consistently surpassing 99% across all datasets. Additionally, BukaGini demonstrates an average reduction in dimensionality of 25%, selecting 10 features for each dataset using the Gini index. Through rigorous comparative analysis with existing methodologies, BukaGini emerges as a promising solution for feature interaction analysis within cybersecurity applications, particularly in the context of IDS. These findings highlight the potential of BukaGini to contribute to robust model performance and propel intrusion detection capabilities to new heights in real-world scenarios.
dc.identifier.citationBouke, M. A., Abdullah, A., Cengiz, K., & Akleylek, S. (2024). Application of BukaGini algorithm for enhanced feature interaction analysis in intrusion detection systems. PeerJ Computer Science, 10, e2043.
dc.identifier.doi10.7717/PEERJ-CS.2043
dc.identifier.issn23765992
dc.identifier.scopus2-s2.0-85192704761
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.7717/PEERJ-CS.2043
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6266
dc.identifier.wosWOS:001266074300007
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorCengiz, Korhan
dc.institutionauthorAkleylek , Sedat
dc.institutionauthoridCengiz Korhan / 0000-0001-6594-8861
dc.institutionauthoridSedat Akleylek / 0000-0001-7005-6489
dc.language.isoen
dc.publisherTaylor & Francis
dc.relation.ispartofPeerJ Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBukaGini Algorithm
dc.subjectCybersecurity Metrics
dc.subjectEnsemble Learning Models
dc.subjectFeature interaction analysis
dc.subjectIntrusion detection systems
dc.titleApplication of BukaGini algorithm for enhanced feature İnteraction analysis in intrusion detection systems
dc.typeArticle

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