Building Multiclass Classification Baselines for Anomaly-based Network Intrusion Detection Systems

This paper showcases multiclass classification baselines using different machine learning algorithms and neural networks for distinguishing legitimate network traffic from direct and obfuscated network intrusions. This research derives its baselines from Advanced Security Network Metrics & Tunne...

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Bibliographic Details
Published in2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) pp. 759 - 760
Main Authors Shah, Ajay, Clachar, Sophine, Minimair, Manfred, Cook, Davis
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2020
Subjects
Anomaly-based intrusion detection system
Biological neural networks
Computational modeling
Computer science
Feature extraction
Measurement
multiclass classification
Network intrusion detection
Network Intrusion Detection System
Signature-based Intrusion Detection System
Telecommunication traffic
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DOI10.1109/DSAA49011.2020.00102

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Summary:This paper showcases multiclass classification baselines using different machine learning algorithms and neural networks for distinguishing legitimate network traffic from direct and obfuscated network intrusions. This research derives its baselines from Advanced Security Network Metrics & Tunneling Obfuscations dataset. The dataset captured legitimate and obfuscated malicious TCP communications on selected vulnerable network services. The multiclass classification NIDS is able to distinguish obfuscated and direct network intrusion with up to 95% accuracy.
DOI:10.1109/DSAA49011.2020.00102