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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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Title
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System
Author
Tama, Bayu AdhiComuzzi, MarcoRhee, Kyung-Hyune
Issue Date
2019-07
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE ACCESS, v.7, pp.94497 - 94507
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.
URI
https://scholarworks.unist.ac.kr/handle/201301/27018
URL
https://ieeexplore.ieee.org/document/8759867
DOI
10.1109/access.2019.2928048
ISSN
2169-3536
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SME_Journal Papers
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