A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets

Smitha, R and Kundapur, Poornima Panduranga and Hareesha, K S (2020) A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets. security and communication networks. ISSN 1939-0122

[img] PDF
9505.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy


)e 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. )erefore, 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 realnetwork 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)

Item Type: Article
Subjects: Engineering > MIT Manipal > MCA
Depositing User: MIT Library
Date Deposited: 25 Sep 2020 08:58
Last Modified: 25 Sep 2020 08:58
URI: http://eprints.manipal.edu/id/eprint/155711

Actions (login required)

View Item View Item