Development of Various Stacking Ensemble Based HIDS Using ADFA Datasets

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dc.authorscopusidSedat Akleylek / 15833929800
dc.authorwosidSedat Akleylek / N-2620-2019
dc.contributor.authorSatılmış, Hami
dc.contributor.authorAkleylek, Sedat
dc.contributor.authorTok, Zaliha Yüce
dc.date.accessioned2025-04-18T09:03:30Z
dc.date.available2025-04-18T09:03:30Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThe rapid increase in the number of cyber attacks and the emergence of various attack variations pose significant threats to the security of computer systems and networks. Various intrusion detection systems (IDS) are developed to defend computer systems and networks in response to these threats. One type of IDS, known as a host-based intrusion detection system (HIDS), focuses on securing a single host. Numerous HIDS have been proposed in the literature, incorporating various detection methods. This study develops multiple machine learning (ML) models and stacking ensemble based HIDS that can be used as detection methods in HIDS. Initially, n-grams, standard bag-of-words (BoW), binary BoW, probability BoW, and term frequency-inverse document frequency (TF-IDF) BoW methods are applied to the ADFA-LD and ADFA-WD datasets. Mutual information and k-means methods are used together for feature selection on the resulting BoW datasets. Individual models are created using either selected features or all features. Subsequently, the outputs of these individual models are used in extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost) models to develop stacking ensemble based models. The experimental results show that the best accuracy (ACC) among models using ADFA-LD based BoW datasets is achieved by the stacking ensemble based XGBoost model, which has an ACC of 0.9747. This XGBoost model utilizes the standard BoW dataset and selected features. Among models using ADFA-WD based BoW datasets, the stacking ensemble based XGBoost is also the most successful in terms of ACC, with an ACC of 0.9163, using the standard BoW dataset and all features. © 2020 IEEE.
dc.identifier.citationSatilmiş, H., Akleylek, S., & Tok, Z. Y. (2025). Development of Various Stacking Ensemble Based HIDS Using ADFA Datasets. IEEE Open Journal of the Communications Society.
dc.identifier.doi10.1109/OJCOMS.2025.3538101
dc.identifier.issn2644125X
dc.identifier.scopus2-s2.0-85217487776
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1109/OJCOMS.2025.3538101
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6676
dc.indekslendigikaynakScopus
dc.institutionauthorAkleylek, Sedat
dc.institutionauthoridSedat Akleylek / 0000-0001-7005-6489
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Open Journal of the Communications Society
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectHost-based Intrusion Detection System
dc.subjectInformation Security
dc.subjectIntrusion Detection System
dc.subjectMachine Learning
dc.titleDevelopment of Various Stacking Ensemble Based HIDS Using ADFA Datasets
dc.typeArticle

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