Patents by Inventor Fumei Lam

Fumei Lam 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).

  • Detecting anomalies in a computer network based on usage similarity scores
    Patent number: 10911470
    Abstract: A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
    Type: Grant
    Filed: September 24, 2019
    Date of Patent: February 2, 2021
    Assignee: SPLUNK INC.
    Inventors: Sudhakar Muddu, Christos Tryfonas, Fumei Lam, Georgios Apostolopoulos
  • Detecting Anomalies in a Computer Network Based on Usage Similarity Scores
    Publication number: 20200021607
    Abstract: A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
    Type: Application
    Filed: September 24, 2019
    Publication date: January 16, 2020
    Inventors: Sudhakar Muddu, Christos Tryfonas, Fumei Lam, Georgios Apostolopoulos
  • Lateral movement detection for network security analysis
    Patent number: 10476898
    Abstract: A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
    Type: Grant
    Filed: May 31, 2018
    Date of Patent: November 12, 2019
    Assignee: SPLUNK INC.
    Inventors: Sudhakar Muddu, Christos Tryfonas, Fumei Lam, Georgios Apostolopoulos
  • LATERAL MOVEMENT DETECTION FOR NETWORK SECURITY ANALYSIS
    Publication number: 20180288079
    Abstract: A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
    Type: Application
    Filed: May 31, 2018
    Publication date: October 4, 2018
    Inventors: Sudhakar Muddu, Christos Tryfonas, Fumei Lam, Georgios Apostolopoulos
  • Lateral movement detection for network security analysis
    Patent number: 10015177
    Abstract: A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
    Type: Grant
    Filed: October 30, 2015
    Date of Patent: July 3, 2018
    Assignee: SPLUNK INC.
    Inventors: Sudhakar Muddu, Christos Tryfonas, Fumei Lam, Georgios Apostolopoulos
  • Lateral Movement Detection for Network Security Analysis
    Publication number: 20170063911
    Abstract: A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
    Type: Application
    Filed: October 30, 2015
    Publication date: March 2, 2017
    Inventors: Sudhakar Muddu, Christos Tryfonas, Fumei Lam, Georgios Apostolopoulos