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Most current network Intrusion Detection Systems (IDSs) 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. This paper presents a new density-based and grid-based clustering algorithm that is suitable for unsupervised anomaly detection.

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Format:PDFSize:224 KB
Date:Jan 2007
Pages:10

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