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Adhi Tama, Bayu
데이터 사이언스 그룹
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A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection

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dc.contributor.authorNkenyereye, Lewis-
dc.contributor.authorBayu Adhi Tama-
dc.contributor.authorLim, Sunghoon-
dc.date.accessioned2021-01-21T01:50:01Z-
dc.date.accessioned2021-01-21T01:50:01Z-
dc.date.available2021-01-21T01:50:01Z-
dc.date.available2021-01-21T01:50:01Z-
dc.date.created2020-12-30-
dc.date.issued2021-
dc.identifier.issn1546-2218-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/9079-
dc.description.abstractAn anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study is to emphasize the effectiveness of stacking ensemble-based model for A-IDS, where deep learning (e.g., deep neural network [DNN]) is used as base learner model. The effectiveness of the proposed model and base DNN model are benchmarked empirically in terms of several performance metrics, i.e., Matthew's correlation coefficient, accuracy, and false alarm rate. The results indicate that the proposed model is superior to the base DNN model as well as other existing ML algorithms found in the literature.-
dc.description.uri1-
dc.language영어-
dc.publisherTECH SCIENCE PRESS-
dc.titleA Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000594856200021-
dc.identifier.scopusid2-s2.0-85097162467-
dc.identifier.rimsid74158-
dc.contributor.affiliatedAuthorBayu Adhi Tama-
dc.identifier.doi10.32604/cmc.2020.012432-
dc.identifier.bibliographicCitationCMC-COMPUTERS MATERIALS & CONTINUA, v.66, no.2, pp.2217 - 2227-
dc.citation.titleCMC-COMPUTERS MATERIALS & CONTINUA-
dc.citation.volume66-
dc.citation.number2-
dc.citation.startPage2217-
dc.citation.endPage2227-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.subject.keywordPlusINTRUSION DETECTION SYSTEM-
dc.subject.keywordPlusFEATURE-SELECTION-
dc.subject.keywordPlusLEARNING APPROACH-
dc.subject.keywordPlusDETECTION MODEL-
dc.subject.keywordPlusMACHINE-
dc.subject.keywordPlusENSEMBLE-
dc.subject.keywordAuthorAnomaly detection-
dc.subject.keywordAuthordeep neural network-
dc.subject.keywordAuthorintrusion detection system-
dc.subject.keywordAuthorstacking ensemble-
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
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