A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection
DC Field | Value | Language |
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dc.contributor.author | Nkenyereye, Lewis | - |
dc.contributor.author | Bayu Adhi Tama | - |
dc.contributor.author | Lim, Sunghoon | - |
dc.date.accessioned | 2021-01-21T01:50:01Z | - |
dc.date.accessioned | 2021-01-21T01:50:01Z | - |
dc.date.available | 2021-01-21T01:50:01Z | - |
dc.date.available | 2021-01-21T01:50:01Z | - |
dc.date.created | 2020-12-30 | - |
dc.date.issued | 2021-11 | - |
dc.identifier.issn | 1546-2218 | - |
dc.identifier.uri | https://pr.ibs.re.kr/handle/8788114/9079 | - |
dc.description.abstract | An 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.language | 영어 | - |
dc.publisher | TECH SCIENCE PRESS | - |
dc.title | A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.identifier.wosid | 000594856200021 | - |
dc.identifier.scopusid | 2-s2.0-85097162467 | - |
dc.identifier.rimsid | 74158 | - |
dc.contributor.affiliatedAuthor | Bayu Adhi Tama | - |
dc.identifier.doi | 10.32604/cmc.2020.012432 | - |
dc.identifier.bibliographicCitation | CMC-COMPUTERS MATERIALS & CONTINUA, v.66, no.2, pp.2217 - 2227 | - |
dc.relation.isPartOf | CMC-COMPUTERS MATERIALS & CONTINUA | - |
dc.citation.title | CMC-COMPUTERS MATERIALS & CONTINUA | - |
dc.citation.volume | 66 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 2217 | - |
dc.citation.endPage | 2227 | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.subject.keywordPlus | INTRUSION DETECTION SYSTEM | - |
dc.subject.keywordPlus | FEATURE-SELECTION | - |
dc.subject.keywordPlus | LEARNING APPROACH | - |
dc.subject.keywordPlus | DETECTION MODEL | - |
dc.subject.keywordPlus | MACHINE | - |
dc.subject.keywordPlus | ENSEMBLE | - |
dc.subject.keywordAuthor | Anomaly detection | - |
dc.subject.keywordAuthor | deep neural network | - |
dc.subject.keywordAuthor | intrusion detection system | - |
dc.subject.keywordAuthor | stacking ensemble | - |