| Title | Building Multiclass Classification Baselines for Anomaly-based Network Intrusion Detection Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Shah, A., Clachar, S., Minimair, M., Cook, D. |
| Conference Name | 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) |
| Keywords | Advanced Security Network Metrics & Tunneling Obfuscations dataset, anomaly-based intrusion detection system, anomaly-based Network intrusion detection systems, Biological neural networks, composability, Computational modeling, computer network security, Computer science, direct network intrusion, direct network intrusions, feature extraction, invasive software, learning (artificial intelligence), legitimate network traffic, legitimate TCP communications, Measurement, Metrics, multiclass classification, multiclass classification baselines, multiclass classification NIDS, network intrusion detection, network intrusion detection system, Neural networks, obfuscated malicious TCP communications, obfuscated network intrusions, pattern classification, pubcrawl, resilience, Resiliency, selected vulnerable network services, Signature-based Intrusion Detection System, telecommunication traffic, transport protocols |
| Abstract | 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 |
| Citation Key | shah_building_2020 |
Building Multiclass Classification Baselines for Anomaly-based Network Intrusion Detection Systems