Building Multiclass Classification Baselines for Anomaly-based Network Intrusion Detection Systems
Ajay Shah, Sophine Clachar, Manfred Minimair, Davis Cook
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.
Topics & Concepts
Computer scienceMulticlass classificationIntrusion detection systemArtificial intelligenceMachine learningArtificial neural networkNetwork securityData miningAnomaly detectionClass (philosophy)Support vector machineComputer networkNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques