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AssigneeThe Trustess of Columbia University in the City of New York
InventorsEleazar Eskin, Andrew Oliver Arnold, Michael Prerau, Leonid Portnoy, Salvatore J. Stolfo
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A method for unsupervised anomaly detection, which are algorithms that are designed to process unlabeled data. Data elements are mapped to a feature space which is typically a vector space . Anomalies are detected by determining which points lies in sparse regions of the feature space. Two feature maps are used for mapping data elements to a feature apace. A first map is a data-dependent normalization feature map which we apply to network connections. A second feature map is a spectrum kernel which we apply to system call traces.
A method for unsupervised detection of an anomaly in the operation of a computer system comprising the steps of:
A method for unsupervised detection of an anomaly in the operation of a computer system based on a set of unlabeled data instances corresponding to computer operation and having features, comprising the steps of
A method for unsupervised detection of an anomaly in the operation of a computer system based on a set of unlabeled data instances corresponding to computer operation and having features, comprising the step of:
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