Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (6): 2253-2258.doi: 10.13229/j.cnki.jdxbgxb20200814

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Vulnerability detection of instant messaging network protocol based on passive clustering algorithm

Jie ZHANG1(),Wen JING1,Fu CHEN2   

  1. 1.School of Computer and Network Engineering，Shanxi Datong University，Datong 037009，China
    2.School of Mathematics and Statistics，Shanxi Datong University，Datong 037009，China.
  • Received:2020-10-26 Online:2021-11-01 Published:2021-11-15

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Abstract:

In order to effectively mine network protocol vulnerabilities， prevent malicious attackers from divulging protocol confidential information， and maintain the security of protocol running environment， a vulnerability detection method of instant messaging network protocol based on passive clustering algorithm is proposed. In passive clustering algorithm， the first declarator priority mechanism is used to select the cluster head， and the gateway node is defined according to the balance principle between network robustness and energy efficiency； the protocol to be detected is formally defined to obtain the detailed workflow of the protocol； the positive and negative samples of the protocol are oversampled by AFL fuzzy detection tool to obtain the complete sample set， and the forward feedback network and support vector are obtained The machine is regarded as the generation model and the discrimination model in the generative adversary network， and the detection case data is obtained by using Lagrange algorithm， which is substituted into the protocol system to complete the vulnerability detection. Simulation results show that the proposed method has high accuracy and efficiency of vulnerability detection， and can effectively ensure the security of network protocol operation.

Key words: passive clustering, instant messaging network, protocol vulnerability, vulnerability detection, deep learning

CLC Number: 

  • TP357

Fig.1

Protocol authentication flow chart"

Fig.2

Schematic diagram of feedforward neural network model"

Fig.3

Vulnerability detection structure"

Fig.4

Comparison of vulnerability numbers of different detection methods"

Table 1

Vulnerability detection efficiency of three methods"

实验次数文献［3］文献［4］本文方法
28.98.55.4
49.19.55.8
610.19.75.6
810.29.85.7
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