Patents by Inventor Yi-Hsun Wang

Yi-Hsun Wang 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).

  • Patent number: 8505080
    Abstract: A method for generating a cross-site scripting attack is provided. An attack string sample is analyzed for obtaining a token sequence. A string word corresponding to each token is used to replace the token for generating a cross-site scripting attack string. Accordingly, a large number of cross-site scripting attacks are generated automatically, so as to execute a penetration test for a website.
    Type: Grant
    Filed: November 17, 2011
    Date of Patent: August 6, 2013
    Assignee: National Taiwan University of Science and Technology
    Inventors: Hahn-Ming Lee, Yi-Hsun Wang, Kuo-Ping Wu, Ching-Hao Mao, Jerome Yeh
  • Publication number: 20130055400
    Abstract: A method for generating a cross-site scripting attack is provided. An attack string sample is analyzed for obtaining a token sequence. A string word corresponding to each token is used to replace the token for generating a cross-site scripting attack string. Accordingly, a large number of cross-site scripting attacks are generated automatically, so as to execute a penetration test for a website.
    Type: Application
    Filed: November 17, 2011
    Publication date: February 28, 2013
    Applicant: National Taiwan University of Science and Technology
    Inventors: Hahn-Ming Lee, Yi-Hsun Wang, Kuo-Ping Wu, Ching-Hao Mao, Jerome Yeh
  • Patent number: 8307459
    Abstract: A botnet detection system is provided. A bursty feature extractor receives an Internet Relay Chat (IRC) packet value from a detection object network, and determines a bursty feature accordingly. A Hybrid Hidden Markov Model (HHMM) parameter estimator determines probability parameters for a Hybrid Hidden Markov Model according to the bursty feature. A traffic profile generator establishes a probability sequential model for the Hybrid Hidden Markov Model according to the probability parameters and pre-defined network traffic categories. A dubious state detector determines a traffic state corresponding to a network relaying the IRC packet in response to reception of a new IRC packet, determines whether the IRC packet flow of the object network is dubious by applying the bursty feature to the probability sequential model for the Hybrid Hidden Markov Model, and generates a warning signal when the IRC packet flow is regarded as having a dubious traffic state.
    Type: Grant
    Filed: March 17, 2010
    Date of Patent: November 6, 2012
    Assignee: National Taiwan University of Science and Technology
    Inventors: Hahn-Ming Lee, Ching-Hao Mao, Yu-Jie Chen, Yi-Hsun Wang, Jerome Yeh, Tsu-Han Chen
  • Publication number: 20110004936
    Abstract: A botnet detection system is provided. A bursty feature extractor receives an Internet Relay Chat (IRC) packet value from a detection object network, and determines a bursty feature accordingly. A Hybrid Hidden Markov Model (HHMM) parameter estimator determines probability parameters for a Hybrid Hidden Markov Model according to the bursty feature. A traffic profile generator establishes a probability sequential model for the Hybrid Hidden Markov Model according to the probability parameters and pre-defined network traffic categories. A dubious state detector determines a traffic state corresponding to a network relaying the IRC packet in response to reception of a new IRC packet, determines whether the IRC packet flow of the object network is dubious by applying the bursty feature to the probability sequential model for the Hybrid Hidden Markov Model, and generates a warning signal when the IRC packet flow is regarded as having a dubious traffic state.
    Type: Application
    Filed: March 17, 2010
    Publication date: January 6, 2011
    Applicant: NATIONAL TAIWAN UNIVERSITY OF SCIENCE & TECHNOLOGY
    Inventors: Hahn-Ming Lee, Ching-Hao Mao, Yu-Jie Chen, Yi-Hsun Wang, Jerome Yeh, Tsu-Han Chen