发明名称 |
Methods of unsupervised anomaly detection using a geometric framework |
摘要 |
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.
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申请公布号 |
US8544087(B1) |
申请公布日期 |
2013.09.24 |
申请号 |
US20080022425 |
申请日期 |
2008.01.30 |
申请人 |
ESKIN ELEAZAR;ARNOLD ANDREW OLIVER;PRERAU MICHAEL;PORTNOY LEONID;STOLFO SALVATORE J.;THE TRUSTESS OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK |
发明人 |
ESKIN ELEAZAR;ARNOLD ANDREW OLIVER;PRERAU MICHAEL;PORTNOY LEONID;STOLFO SALVATORE J. |
分类号 |
G06F12/14;G06F12/16 |
主分类号 |
G06F12/14 |
代理机构 |
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