Unsupervised Anomaly Detection in Network Intrusion Detection Using Clusters

  title={Unsupervised Anomaly Detection in Network Intrusion Detection Using Clusters},
  author={Kingsly Leung and Christopher Leckie},
Most current network intrusion detection systems employ signature-based methods or data mining-based methods which rely on labelled training data. This training data is typically expensive to produce. Moreover, these methods have difficulty in detecting new types of attack. Using unsupervised anomaly detection techniques, however, the system can be trained with unlabelled data and is capable of detecting previously “unseen” attacks. In this paper, we present a new density-based and grid-based… CONTINUE READING
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Intru - sion detection in unlabeled data with quarter - sphere support vector machines

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