A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets

• Published in: Security and communication networks Vol. 2020; no. 2020; pp. 1 - 9
• Main Authors: Rajagopal, Smitha, Hareesha, Katiganere Siddaramappa, Kundapur, Poornima Panduranga
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; Hindawi Limited
• 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
• ISSN: 1939-0114; 1939-0122
• DOI: 10.1155/2020/4586875

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).