Patents by Inventor Naveen Selvan

Naveen Selvan 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).

  • Utilizing Machine Learning to detect malicious executable files efficiently and effectively
    Publication number: 20240028721
    Abstract: Systems and methods include performing inline monitoring of production traffic between users, the Internet, and cloud services via a cloud-based system; utilizing a trained machine learning model to inspect static properties of files in the production traffic; and classifying the traffic as one of malicious or benign based on the trained machine learning model.
    Type: Application
    Filed: September 26, 2023
    Publication date: January 25, 2024
    Inventors: Changsha Ma, Nirmal Singh, Naveen Selvan, Tarun Dewan, Uday Pratap Singh, Deepen Desai, Bharath Meesala, Rakshitha Hedge, Parnit Sainion, Shashank Gupta, Narinder Paul, Rex Shang, Howie Xu
  • Utilizing Machine Learning to detect malicious executable files efficiently and effectively
    Patent number: 11803641
    Abstract: Systems and methods include determining a plurality of features associated with executable files, wherein the plurality of features are each based on static properties in predefined structure of the executable files; obtaining training data that includes samples of benign executable files and malicious executable files; extracting the plurality of features from the training data; and utilizing the extracted plurality of features to train a machine learning model to detect malicious executable files.
    Type: Grant
    Filed: October 26, 2020
    Date of Patent: October 31, 2023
    Assignee: Zscaler, Inc.
    Inventors: Changsha Ma, Nirmal Singh, Naveen Selvan, Tarun Dewan, Uday Pratap Singh, Deepen Desai, Bharath Meesala, Rakshitha Hedge, Parnit Sainion, Shashank Gupta, Narinder Paul, Rex Shang, Howie Xu
  • Utilizing Machine Learning to detect malicious executable files efficiently and effectively
    Publication number: 20220083659
    Abstract: Systems and methods include determining a plurality of features associated with executable files, wherein the plurality of features are each based on static properties in predefined structure of the executable files; obtaining training data that includes samples of benign executable files and malicious executable files; extracting the plurality of features from the training data; and utilizing the extracted plurality of features to train a machine learning model to detect malicious executable files.
    Type: Application
    Filed: October 26, 2020
    Publication date: March 17, 2022
    Inventors: Changsha Ma, Nirmal Singh, Naveen Selvan, Tarun Dewan, Uday Pratap Singh, Deepen Desai, Bharath Meesala, Rakshitha Hedge, Parnit Sainion, Shashank Gupta, Narinder Paul, Rex Shang, Howie Xu
  • Utilizing Machine Learning to detect malicious Office documents
    Publication number: 20220083661
    Abstract: Systems and methods include, based on monitoring of content including Office documents, determining distribution of malicious Office documents between documents having malicious macros and documents having malicious embedded objects; determining features for the documents having malicious macros and for the documents having malicious embedded objects; selecting training data for a machine learning model based on the distribution and the features; and training the machine learning model with the selected training data.
    Type: Application
    Filed: October 26, 2020
    Publication date: March 17, 2022
    Inventors: Changsha Ma, Nirmal Singh, Naveen Selvan, Tarun Dewan, Uday Pratap Singh, Deepen Desai, Bharath Meesala, Rakshitha Hedge, Parnit Sainion, Shashank Gupta, Narinder Paul, Rex Shang, Howie Xu
  • Machine learning to determine command and control sites
    Publication number: 20210377304
    Abstract: Systems and methods include receiving a domain for a determination of a likelihood the domain is a command and control site; analyzing the domain with an ensemble of a plurality of trained machine learning models including a Uniform Resource Locator (URL) model that analyzes lexical features of a hostname of the domain and an artifact model that analyzes content features of a webpage associated with the domain; and combining results of the ensemble to predict the likelihood the domain is a command and control site.
    Type: Application
    Filed: June 8, 2021
    Publication date: December 2, 2021
    Inventors: Changsha Ma, Loc Bui, Dianhuan Lin, Rex Shang, Bryan Lee, Shudong Zhou, Howie Xu, Naveen Selvan, Nirmal Singh, Deepen Desai, Parnit Sainion, Narinder Paul