A Stacking Ensemble Learning Approach for Intrusion Detection System

Basit Öğe Kaydı
dc.contributor.authorUçar, Murat
dc.contributor.authorİncetaş, Mürsel Ozan
dc.contributor.authorUçar, Emine
dc.date.accessioned2023-04-10T20:27:55Z
dc.date.available2023-04-10T20:27:55Z
dc.date.issued2021
dc.departmentRektörlük, Rektörlüğe Bağlı Birimler, Düzce Üniversitesi Dergilerien_US
dc.description.abstractIntrusion detection systems (IDSs) have received great interest in computer science, along with increased network productivity and security threats. The purpose of this study is to determine whether the incoming network traffic is normal or an attack based on 41 features in the NSL-KDD dataset. In this paper, the performance of a stacking technique for network intrusion detection was analysed. Stacking technique is an ensemble approach which is used for combining various classification methods to produce a preferable classifier. Stacking models were trained on the NSLKDD training dataset and evaluated on the NSLKDDTest+ and NSLKDDTest21 test datasets. In the stacking technique, four different algorithms were used as base learners and an algorithm was used as a stacking meta learner. Logistic Regression (LR), Decision Trees (DT), Artificial Neural Networks (ANN), and K Nearest Neighbor (KNN) are the base learner models and Support Vector Machine (SVM) model is the meta learner. The proposed models were evaluated using accuracy rate and other performance metrics of classification. Experimental results showed that stacking significantly improved the performance of intrusion detection systems. The ensemble classifier (DT-LR-ANN + SVM) model achieved the best accuracy results with 90.57% in the NSLKDDTest + dataset and 84.32% in the NSLKDDTest21 dataset.en_US
dc.identifier.doi10.29130/dubited.737211
dc.identifier.endpage1341en_US
dc.identifier.issn2148-2446
dc.identifier.issue4en_US
dc.identifier.startpage1329en_US
dc.identifier.trdizinid498549en_US
dc.identifier.urihttp://doi.org/10.29130/dubited.737211
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/498549
dc.identifier.urihttps://hdl.handle.net/20.500.12684/11807
dc.identifier.volume9en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofDüzce Üniversitesi Bilim ve Teknoloji Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleA Stacking Ensemble Learning Approach for Intrusion Detection Systemen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
11807.pdf
Boyut:
1.01 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

TR-Dizin İndeksli Yayınlar Koleksiyonu
Düzce Üniversitesi Bilim ve Teknoloji Dergisi Koleksiyonu