Feilong Chen

Feilong Chen

Charlotte Metro
2K followers 500+ connections

About

With over eight years of experience in full-stack development and API design, I am a…

Activity

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Experience

  • HubSpot Graphic

    HubSpot

    United States

  • -

    Charlotte, North Carolina Area

  • -

    Charlotte, North Carolina

  • -

    Charlotte, North Carolina Area

  • -

    Charlotte, North Carolina Area

  • -

    Greater New York City Area

  • -

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    San Jose, CA

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Education

Volunteer Experience

  • Overseas China Education Foundation Graphic

    Online Fundraising

    Overseas China Education Foundation

    - Present 14 years 11 months

    Children

    Mainly responsible for promoting OCEF's mission and works, and writing and setting up fundraisers at
    http://www.causes.com/ocef
    http://www.mitbbs.com/bbsdoc/mitOCEF.html

    OCEF is an independent, non-governmental, non-political and non-religious organization. Its sole mission is to help underprivileged children in impoverished rural areas of China receive education and improve the quality of education in rural China. Operated primarily by unpaid volunteers, OCEF keeps operational…

    Mainly responsible for promoting OCEF's mission and works, and writing and setting up fundraisers at
    http://www.causes.com/ocef
    http://www.mitbbs.com/bbsdoc/mitOCEF.html

    OCEF is an independent, non-governmental, non-political and non-religious organization. Its sole mission is to help underprivileged children in impoverished rural areas of China receive education and improve the quality of education in rural China. Operated primarily by unpaid volunteers, OCEF keeps operational costs minimal. Till 2012 spring, OCEF has sponsred 39,092 students, 470 rural libraries, with the operating costs accounted for 4.6% of annual donation receipts.

Patents

  • Machine learning based botnet detection using real-time connectivity graph based traffic features

    Issued USPTO 08762298

    A method for identifying a botnet in a network, including analyzing historical network data using a pre-determined heuristic to determine values of a connectivity graph based feature in the historical network data, obtaining a ground truth data set having labels assigned to data units in the historical network data identifying known malicious nodes in the network, analyzing the historical network data and the ground truth data set using a machine learning algorithm to generate a model…

    A method for identifying a botnet in a network, including analyzing historical network data using a pre-determined heuristic to determine values of a connectivity graph based feature in the historical network data, obtaining a ground truth data set having labels assigned to data units in the historical network data identifying known malicious nodes in the network, analyzing the historical network data and the ground truth data set using a machine learning algorithm to generate a model representing the labels as a function of the values of the connectivity graph based feature, analyzing real-time network data using the pre-determined heuristic to determine a value of the connectivity graph based feature for a data unit in the real-time network data, assigning a label to the data unit by applying the model to the value of the connectivity graph based feature, and categorizing the data unit as associated with the botnet based on the label.

    Other inventors
    See patent
  • Machine learning based botnet detection with dynamic adaptation

    US 8402543

    Other inventors

Honors & Awards

  • Shared Success Award

    -

    In recognition of Teamwork & Partnership during a project collaborating with SVC DEL team.

Languages

  • Chinese

    Native or bilingual proficiency

  • Cantonese

    Native or bilingual proficiency

  • English

    Professional working proficiency

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