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Anonymous network traffic fingerprint identification method and equipment based on time convolutional network

An anonymous network and convolutional network technology, applied in the Internet field, can solve the problems of high cost and low accuracy of anonymous network identification technology, and achieve the effect of accurate extraction of traffic characteristics and improvement of accuracy

Active Publication Date: 2021-06-04
BEIJING UNIV OF POSTS & TELECOMM
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] In view of this, the purpose of one or more embodiments of this specification is to propose a method and device for anonymous network traffic fingerprint identification based on time convolution network to solve the problem of high cost and low accuracy of existing anonymous network identification technology

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  • Anonymous network traffic fingerprint identification method and equipment based on time convolutional network
  • Anonymous network traffic fingerprint identification method and equipment based on time convolutional network
  • Anonymous network traffic fingerprint identification method and equipment based on time convolutional network

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Embodiment Construction

[0054] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0055] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present specification shall have ordinary meanings understood by those skilled in the art to which the present disclosure belongs. "First", "second" and similar words used in one or more embodiments of the present specification do not indicate any order, quantity or importance, but are only used to distinguish different components. "Comprising" or "comprising" and similar words mean that the elements or items appearing before the word include the elements or items listed after the word and their equivalents, without excluding other elements or items. Words such as "connected" or "con...

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Abstract

One or more embodiments of the invention provide an anonymous network traffic fingerprint identification method and equipment based on a time convolutional network. The method comprises the following steps: acquiring a traffic data packet of an anonymous network accessed by a target user; analyzing the traffic data packet, and generating a direction sequence of a basic unit layer of the traffic data packet, a time sequence of the basic unit layer, a cumulative feature sequence of the direction sequence and a cumulative feature sequence of the time sequence; combining the direction sequence of the basic unit layer with the cumulative feature sequence of the direction sequence, combining the time sequence of the basic unit layer with the cumulative feature sequence of the time sequence, and respectively inputting the combined sequences into different time convolutional networks in a pre-constructed anonymous network flow fingerprint identification model, and outputting the category of the anonymous network accessed by the target user. According to the method, the direction sequence and the time sequence of the flow data of the anonymous network are fully utilized, and the accuracy of identifying the anonymous network flow fingerprint is improved.

Description

technical field [0001] One or more embodiments of this specification relate to the field of Internet technology, and in particular to a method and device for identifying anonymous network traffic fingerprints based on time convolutional networks. Background technique [0002] Anonymous network traffic fingerprinting technology refers to whether a local and passive user can determine the purpose of anonymous network user access by monitoring the traffic of the client. Traffic fingerprinting means that local and passive users collect the observable traffic of the target user's client as a data packet sequence, apply machine learning classification to the client's data packet sequence, and then guess which web page the target user has visited. [0003] Existing website fingerprinting technologies, among them, technologies based on machine learning methods require manual selection of features, which are mainly based on intuition, experience, and professional knowledge about how ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/06G06N3/08G06N3/04
CPCH04L63/0421H04L63/1408H04L63/0876G06N3/08G06N3/045
Inventor 时金桥张尼苏马婧王学宾王美琪宋栋石瑞生王东滨
Owner BEIJING UNIV OF POSTS & TELECOMM