Patents by Inventor Kathrin Grosse

Kathrin Grosse 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).

  • Measuring overfitting of machine learning computer model and susceptibility to security threats
    Patent number: 11494496
    Abstract: Mechanisms are provided to determine a susceptibility of a trained machine learning model to a cybersecurity threat. The mechanisms execute a trained machine learning model on a test dataset to generate test results output data, and determine an overfit measure of the trained machine learning model based on the generated test results output data. The overfit measure quantifies an amount of overfitting of the trained machine learning model to a specific sub-portion of the test dataset. The mechanisms apply analytics to the overfit measure to determine a susceptibility probability that indicates a likelihood that the trained machine learning model is susceptible to a cybersecurity threat based on the determined amount of overfitting of the trained machine learning model. The mechanisms perform a corrective action based on the determined susceptibility probability.
    Type: Grant
    Filed: March 30, 2020
    Date of Patent: November 8, 2022
    Assignee: International Business Machines Corporation
    Inventors: Kathrin Grosse, Taesung Lee, Youngja Park, Ian Michael Molloy
  • Measuring Overfitting of Machine Learning Computer Model and Susceptibility to Security Threats
    Publication number: 20210303695
    Abstract: Mechanisms are provided to determine a susceptibility of a trained machine learning model to a cybersecurity threat. The mechanisms execute a trained machine learning model on a test dataset to generate test results output data, and determine an overfit measure of the trained machine learning model based on the generated test results output data. The overfit measure quantifies an amount of overfitting of the trained machine learning model to a specific sub-portion of the test dataset. The mechanisms apply analytics to the overfit measure to determine a susceptibility probability that indicates a likelihood that the trained machine learning model is susceptible to a cybersecurity threat based on the determined amount of overfitting of the trained machine learning model. The mechanisms perform a corrective action based on the determined susceptibility probability.
    Type: Application
    Filed: March 30, 2020
    Publication date: September 30, 2021
    Inventors: Kathrin Grosse, Taesung Lee, Youngja Park, Ian Michael Molloy