Unsupervised network traffic monitoring is of paramount importance in cyber security. It allows to detect suspicious events that are defined as non-normal and report or block them. In this work the Anomaly Consensus algorithm for unsupervised network analysis is presented. The algorithm aim is to fuse the three most important anomaly detection techniques for unsupervised detection of suspicious events. Tests are performed against the KDD Cup'99 dataset, one of the most famous supervised datasets for automatic intrusion detection created by DARPA. Accuracies reveal that Anomaly Consensus performs on-par with respect to state-of-the-art supervised learning techniques, ensuring high generalization power also in borderline tests when small amount of data (5%) is used for training and the rest is for validation and testing.

Ensemble consensus: An unsupervised algorithm for anomaly detection in network security data

Dentamaro V.;Galantucci S.;Giglio P.;Palmisano T.;Pirlo G.
2021-01-01

Abstract

Unsupervised network traffic monitoring is of paramount importance in cyber security. It allows to detect suspicious events that are defined as non-normal and report or block them. In this work the Anomaly Consensus algorithm for unsupervised network analysis is presented. The algorithm aim is to fuse the three most important anomaly detection techniques for unsupervised detection of suspicious events. Tests are performed against the KDD Cup'99 dataset, one of the most famous supervised datasets for automatic intrusion detection created by DARPA. Accuracies reveal that Anomaly Consensus performs on-par with respect to state-of-the-art supervised learning techniques, ensuring high generalization power also in borderline tests when small amount of data (5%) is used for training and the rest is for validation and testing.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/377597
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