Encrypted anonymous network traffic identification method

An anonymous network and traffic identification technology, applied in the field of encrypted anonymous network traffic identification, can solve the problems of inefficient algorithm and affect the classification effect, and achieve the effect of improving the calculation speed, speeding up the learning speed and improving the prediction accuracy.

Active Publication Date: 2019-06-28
INST OF INFORMATION ENG CAS
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Problems solved by technology

However, during the construction process of the C4.5 algorithm, the data set needs to be scanned and sorted multiple times in sequence, which leads to the inefficiency of the algorithm
At the same time,

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  • Encrypted anonymous network traffic identification method
  • Encrypted anonymous network traffic identification method
  • Encrypted anonymous network traffic identification method

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Embodiment Construction

[0022] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, and to make the purpose, features and advantages of the present invention more obvious and understandable, the technical core of the present invention will be further described in detail below in conjunction with the accompanying drawings . It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0023] In the present invention, an effective encrypted anonymous network traffic identification method is designed. The general idea of ​​the method is to use traffic extraction tools to extract flow features from raw traffic files, and filter the features through a novel hybrid feature selection algorithm to filter out redundant and irrelevant features. Then use the XGBoost algorithm to build a model to classify encrypted anonymous network traffic...

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Abstract

The invention discloses an encrypted anonymous network traffic identification method. The method comprises the following steps: 1) extracting multi-granularity level characteristics of each flow filefrom an encrypted anonymous network flow data set, wherein the multi-granularity level characteristics comprise flow characteristics, packet characteristics, host behavior characteristics, TCP headerrelated characteristics and IP header related characteristics; 2) filtering the features obtained in the step (1), and filtering redundant features and features irrelevant to flow identification or features with the relevancy lower than a set threshold value; and 3) training an XGBoost model by utilizing the characteristics selected in the step 2), and then identifying the anonymous network traffic to be identified by utilizing the XGBoost model. The method is superior to the existing baseline identification method in the aspects of overall accuracy, precision rate, recall rate and F1 value onmodel performance.

Description

technical field [0001] The invention proposes an effective encrypted anonymous network traffic identification method. The method combines a new hybrid feature selection algorithm and extreme gradient boosting (XGBoost) classification algorithm, which belongs to the cross-technical field of combining machine learning and information security. Background technique [0002] As the Internet penetrates into all aspects of society, economy and politics, the situation of Internet security and privacy protection is becoming more and more severe. Traditional information encryption technology can protect the transmission content, but cannot hide the information, geographical location and communication method of the communicating parties. In this context, researchers have proposed a large number of encrypted anonymous network technologies. The more popular low-latency anonymous communication tools at this stage include Tor, I2P, and JonDonym. [0003] Although encrypted anonymous ne...

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Application Information

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IPC IPC(8): H04L29/06
Inventor 蔡真真姜波凌玥卢志刚刘俊荣董聪
Owner INST OF INFORMATION ENG CAS
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