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Comuzzi, Marco
Intelligent Enterprise Lab (IEL)
Research Interests
  • business process management, enterprise systems, process monitoring, compliance

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TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System

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dc.contributor.author Tama, Bayu Adhi ko
dc.contributor.author Comuzzi, Marco ko
dc.contributor.author Rhee, Kyung-Hyune ko
dc.date.available 2019-07-22T00:34:36Z -
dc.date.created 2019-07-15 ko
dc.date.issued 2019-07 ko
dc.identifier.citation IEEE ACCESS, v.7, pp.94497 - 94507 ko
dc.identifier.issn 2169-3536 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/27018 -
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. ko
dc.language 영어 ko
dc.publisher Institute of Electrical and Electronics Engineers Inc. ko
dc.title TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-85073896571 ko
dc.identifier.wosid 000478676600028 ko
dc.type.rims ART ko
dc.identifier.doi 10.1109/access.2019.2928048 ko
dc.identifier.url https://ieeexplore.ieee.org/document/8759867 ko
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