Patents by Inventor Foteini Cheirdari-Argiropoulos

Foteini Cheirdari-Argiropoulos 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).

  • Method and system for reducing false positives in static source code analysis reports using machine learning and classification techniques
    Patent number: 11620389
    Abstract: This invention is a computer-implemented method and system of using a secondary classification algorithm after using a primary source code vulnerability scanning tool to more accurately label true and false vulnerabilities in source code. The method and system use machine learning within a 10% dataset to develop a classifier model algorithm. A selection process identifies the most important features utilized in the algorithm to detect and distinguish the true and false positive findings of the static code analysis results. A personal identifier is used as a critical feature for the classification. The model is validated by experimentation and comparison against thirteen existing classifiers.
    Type: Grant
    Filed: June 24, 2020
    Date of Patent: April 4, 2023
    Assignee: UNIVERSITY OF MARYLAND BALTIMORE COUNTY
    Inventors: George Karabatis, Foteini Cheirdari-Argiropoulos
  • Method and System for Reducing False Positives in Static Source Code Analysis Reports Using Machine Learning and Classification Techniques
    Publication number: 20200401702
    Abstract: This invention is a computer-implemented method and system of using a secondary classification algorithm after using a primary source code vulnerability scanning tool to more accurately label true and false vulnerabilities in source code. The method and system use machine learning within a 10% dataset to develop a classifier model algorithm. A selection process identifies the most important features utilized in the algorithm to detect and distinguish the true and false positive findings of the static code analysis results. A personal identifier is used as a critical feature for the classification. The model is validated by experimentation and comparison against thirteen existing classifiers.
    Type: Application
    Filed: June 24, 2020
    Publication date: December 24, 2020
    Inventors: George Karabatis, Foteini Cheirdari-Argiropoulos