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

IEEE access, 2019, Vol.7, p.94497-94507 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2019.2928048 ;CODEN: IAECCG

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Title:
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System
Author: Tama, Bayu Adhi ; Comuzzi, Marco ; Rhee, Kyung-Hyune
Subjects: Anomaly detection ; Ant colony optimization ; Bagging ; Classification ; Classifiers ; Computer networks ; Computer security ; Datasets ; Error reduction ; Feature extraction ; Feature selection ; Genetic algorithms ; hybrid feature selection ; Internet of Things ; Intrusion detection ; intrusion detection system ; Intrusion detection systems ; network anomaly detection ; Particle swarm optimization ; Statistical significance ; statistical significance test ; Training ; Two-stage meta classifier
Is Part Of: IEEE access, 2019, Vol.7, p.94497-94507
Description: Intrusion detection systems (IDSs) 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 are proposed. A 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 ensemble 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. The 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 the IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier.
Publisher: Piscataway: IEEE
Language: English
Identifier: ISSN: 2169-3536
EISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2928048
CODEN: IAECCG
Source: DOAJ Directory of Open Access Journals
IEEE Xplore Open Access Journals

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

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2019.2928048 ;CODEN: IAECCG

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