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MarcoComuzzi

Comuzzi, Marco
Intelligent Enterprise Lab.
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dc.citation.endPage 94507 -
dc.citation.startPage 94497 -
dc.citation.title IEEE ACCESS -
dc.citation.volume 7 -
dc.contributor.author Tama, Bayu Adhi -
dc.contributor.author Comuzzi, Marco -
dc.contributor.author Rhee, Kyung-Hyune -
dc.date.accessioned 2023-12-21T19:02:22Z -
dc.date.available 2023-12-21T19:02:22Z -
dc.date.created 2019-07-15 -
dc.date.issued 2019-07 -
dc.description.abstract Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier. -
dc.identifier.bibliographicCitation IEEE ACCESS, v.7, pp.94497 - 94507 -
dc.identifier.doi 10.1109/access.2019.2928048 -
dc.identifier.issn 2169-3536 -
dc.identifier.scopusid 2-s2.0-85073896571 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/27018 -
dc.identifier.url https://ieeexplore.ieee.org/document/8759867 -
dc.identifier.wosid 000478676600028 -
dc.language 영어 -
dc.publisher Institute of Electrical and Electronics Engineers Inc. -
dc.title TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System -
dc.type Article -
dc.description.isOpenAccess TRUE -
dc.relation.journalWebOfScienceCategory Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications -
dc.relation.journalResearchArea Computer Science; Engineering; Telecommunications -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor hybrid feature selection -
dc.subject.keywordAuthor intrusion detection system -
dc.subject.keywordAuthor statistical significance test -
dc.subject.keywordAuthor Two-stage meta classifier -
dc.subject.keywordAuthor network anomaly detection -
dc.subject.keywordPlus MACHINE -
dc.subject.keywordPlus INTERNET -
dc.subject.keywordPlus THINGS -
dc.subject.keywordPlus SECURITY -
dc.subject.keywordPlus FOREST -
dc.subject.keywordPlus MODEL -

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