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A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets
Joint Authors
Rajagopal, Smitha
Kundapur, Poornima Panduranga
Hareesha, Katiganere Siddaramappa
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-24
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
The problem of network intrusion detection poses innumerable challenges to the research community, industry, and commercial sectors.
Moreover, the persistent attacks occurring on the cyber-threat landscape compel researchers to devise robust approaches in order to address the recurring problem.
Given the presence of massive network traffic, conventional machine learning algorithms when applied in the field of network intrusion detection are quite ineffective.
Instead, a hybrid multimodel solution when sought improves performance thereby producing reliable predictions.
Therefore, this article presents an ensemble model using metaclassification approach enabled by stacked generalization.
Two contemporary as well as heterogeneous datasets, namely, UNSW NB-15, a packet-based dataset, and UGR’16, a flow-based dataset, that were captured in emulated as well as real network traffic environment, respectively, were used for experimentation.
Empirical results indicate that the proposed stacking ensemble is capable of generating superior predictions with respect to a real-time dataset (97% accuracy) than an emulated one (94% accuracy).
American Psychological Association (APA)
Rajagopal, Smitha& Kundapur, Poornima Panduranga& Hareesha, Katiganere Siddaramappa. 2020. A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1208421
Modern Language Association (MLA)
Rajagopal, Smitha…[et al.]. A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets. Security and Communication Networks No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1208421
American Medical Association (AMA)
Rajagopal, Smitha& Kundapur, Poornima Panduranga& Hareesha, Katiganere Siddaramappa. A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1208421
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208421