Patents by Inventor Leonid Portnoy

Leonid Portnoy has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • METHODS OF UNSUPERVISED ANOMALY DETECTION USING A GEOMETRIC FRAMEWORK
    Publication number: 20160191561
    Abstract: 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 d. 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.
    Type: Application
    Filed: March 8, 2016
    Publication date: June 30, 2016
    Applicant: THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK
    Inventors: Eleazar Eskin, Andrew Oliver Arnold, Michael Prerau, Leonid Portnoy, Salvatore J. Stolfo
  • Methods of unsupervised anomaly detection using a geometric framework
    Patent number: 9306966
    Abstract: 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 d. 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.
    Type: Grant
    Filed: August 20, 2013
    Date of Patent: April 5, 2016
    Assignee: THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK
    Inventors: Eleazar Eskin, Andrew Arnold, Michael Prerau, Leonid Portnoy, Salvatore J. Stolfo
  • Methods of unsupervised anomaly detection using a geometric framework
    Publication number: 20150058982
    Abstract: 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 d. 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.
    Type: Application
    Filed: August 20, 2013
    Publication date: February 26, 2015
    Inventors: Eleazar Eskin, Andrew Arnold, Michael Prerau, Leonid Portnoy, Salvatore J. Stolfo
  • Methods of unsupervised anomaly detection using a geometric framework
    Patent number: 8544087
    Abstract: 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.
    Type: Grant
    Filed: January 30, 2008
    Date of Patent: September 24, 2013
    Assignee: The Trustess of Columbia University in the City of New York
    Inventors: Eleazar Eskin, Andrew Oliver Arnold, Michael Prerau, Leonid Portnoy, Salvatore J. Stolfo