A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets

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. Give...

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Bibliographic Details
Published in:Security and communication networks 2020, Vol.2020 (2020), p.1-9
Main Authors: Rajagopal, Smitha, Hareesha, Katiganere Siddaramappa, Kundapur, Poornima Panduranga
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Bias
Classification
Communications traffic
Cybersecurity
Datasets
Decision making
Decision trees
Experimentation
False alarms
Fuzzy logic
Genetic algorithms
Internet service providers
Intrusion detection systems
Machine learning
Neural networks
Stacking
Support vector machines
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Summary: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).
ISSN:1939-0114
1939-0122
DOI:10.1155/2020/4586875