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

Previous Articles    

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

RICH HTML

 1   

PDF (PC)

106

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
1 冯文博, 洪征, 吴礼发,等. 网络协议识别技术综述[J]. 计算机应用, 2019, 39(12):3604-3614.
Feng Wen-bo, Hong Zheng, Wu Li-fa, et al. Overview of network protocol identification technology [J]. Computer Applications, 2019, 39 (12): 3604-3614.
2 Atilgan C, Nasibov E N. A space efficient minimum spanning tree approach to the fuzzy Joint points clustering algorithm[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(6):1317-1322.
3 邓兆琨, 陆余良, 黄钊, 等. 基于程序建模的网络程序漏洞检测技术[J]. 北京航空航天大学学报, 2019, 45(4):796-803.
Deng Zhao-kun, Lu Yu-liang, Huang Zhao, et al. Network program vulnerability detection technology based on program modeling[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(4):796-803.
4 李佳莉, 陈永乐, 李志, 等. 基于协议状态图遍历的RTSP协议漏洞挖掘[J]. 计算机科学, 2018, 45(9):178-183.
Li Jia-li, Chen Yong-le, Li Zhi, et al. RTSP protocol vulnerability mining based on traversal of protocol state graph [J]. Computer Science, 2018, 45 (9): 178-183.
5 Huang C, Molisch A F, Geng Y A, et al. Trajectory-Joint clustering algorithm for time-varying channel modeling[J]. IEEE Transactions on Vehicular Technology, 2020, 69(1):1041-1045.
6 丁旭, 黄成, 吴晓蓓,等. 基于压缩感知的传感器网络中概率负载均衡的数据路由协议[J]. 控制与决策, 2018, 33(06):1041-1047.
Ding Xu, Huang Cheng, Wu Xiao-bei, et al. Data routing protocol for probabilistic load balancing in sensor networks based on compressed sensing [J]. Control and Decision, 2018, 33 (06): 1041-1047
7 Tan B, Elnaggar R, Fung J M, et al. Towards hardware-based IP vulnerability detection and post-deployment patching in systems-on-chip[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020, PP(99):1-1.
8 夏之阳, 易平, 杨涛. 基于神经网络与代码相似性的静态漏洞检测[J]. 计算机工程, 2019, 45(12):141-146.
Xia Zhi-yang, Yi Ping, Yang Tao. Static vulnerability detection based on neural network and code similarity [J]. Computer Engineering, 2019, 45 (12): 141-146.
9 Nancy P, Muthurajkumar S, Ganapathy S, et al. Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks[J]. IET Communications, 2020, 14(5):888-895.
10 苏彬, 马丽梅, 崔宝江. 适用于传感器采集网络的轻量级安全通信协议[J]. 计算机工程与设计, 2018, 39(5): 1262-1268.
Su Bin, Ma Li-mei, Cui Bao-jiang. Lightweight secure communication protocol for sensor acquisition networks [J]. Computer engineering and design, 2018, 39 (5): 1262-1268.
11 Zhao Z N, Wang J, Guo H W. Analysis of vulnerability propagation for the all-optical network based on Bio-PEPA[J]. Wireless Networks, 2019, 25(6):3513-3529.
12 Nunes P, Medeiros I, Fonseca J, et al. An empirical study on combining diverse static analysis tools for web security vulnerabilities based on development scenarios[J]. Computing, 2019, 101(2):161-185.
13 荆琛, 傅晓彤, 董伟,等. 基于Q-学习算法的有状态网络协议模糊测试方法研究[J]. 电子技术应用, 2020, 46(4):49-52, 56.
Jing Chen, Fu Xiao-tong, Dong Wei, et al. Research on fuzzy testing method of stateful network protocol based on Q-learning algorithm[J]. Electronic Technology Application, 2020, 46 (4): 49-52, 56.
14 He F, Zhang B. A protocol of potential advantage in the low frequency range to gravitational wave detection with space based optical atomic clocks[J]. The European Physical Journal D, 2020, 74(5):1-6.
15 张志华. 基于渗透测试的网络安全漏洞实时侦测技术[J]. 科学技术与工程, 2018, 18(20):297-302.
Zhang Zhi-hua, Real time detection technology of network security vulnerability based on penetration test [J]. Science, technology and engineering, 2018, 18(20): 297-302.
16 Kim J, Cho K, Kim Y K, et al. Study on peak misdetection recovery of key exchange protocol using heartbeat[J]. Journal of supercomputing, 2019, 75(6):3288-3301.
17 王培超, 周鋆, 朱承, 等. 基于贝叶斯网络的XSS攻击检测方法[J]. 中国科学技术大学学报, 2019, 49(2):166-172.
Wang Pei-chao, Zhou Yun, Zhu Cheng, et al. XSS attack detection method based on Bayesian networks [J]. Journal of China University of Science and Technology, 2019, 49 (2): 166-172.
18 Han Y, Seed D, Wang C, et al. Delay-aware application protocol for internet of things[J]. IEEE Network, 2018 (1):1-8.
19 许力, 李光辉. 基于信任机制的无线传感器网络多协议层入侵检测方法[J]. 传感技术学报, 2019, 32(5):101-110.
Xu Li, Li Guang-hui. Multi protocol layer intrusion detection method for wireless sensor networks based on trust mechanism [J]. Journal of sensing technology, 2019, 32 (5): 101-110.
20 Wei X, Gao S, Huang T, et al. Complex network-based cascading faults graph for the analysis of transmission network vulnerability[J]. IEEE Transactions on Industrial Informatics, 2019, 15(3):1265-1276.
[1] Li-li DONG,Dan YANG,Xiang ZHANG. Large⁃scale semantic text overlapping region retrieval based on deep learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1817-1822.
[2] Li-sheng JIN,Bai-cang GUO,Fang-rong WANG,Jian SHI. Dynamic multiple object detection algorithm for vehicle forward based on improved YOLOv3 [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1427-1436.
[3] Feng-chong LAN,Ji-wen LI,Ji-qing CHEN. DG-SLAM algorithm for dynamic scene compound deep learning and parallel computing [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1437-1446.
[4] Jin-qing LI,Jian ZHOU,Xiao-qiang DI. Learning optical image encryption scheme based on CycleGAN [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1060-1066.
[5] Zhe-ming YUAN,Hong-jie YUAN,Yu-xuan YAN,Qian LI,Shuang-qing LIU,Si-qiao TAN. Automatic recognition and classification of field insects based on lightweight deep learning model [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1131-1139.
[6] Bo PENG,Yuan-yuan ZHANG,Yu-ting WANG,Ju TANG,Ji-ming XIE. Automatic traffic state recognition from videos based on auto⁃encoder and classifiers [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 886-892.
[7] Zhen SONG,Jun-liang LI,Gui-qiang LIU. Constant flow prediction method of variable speed hydraulic power source based on deep learning and limitation fuzzy [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1106-1110.
[8] Hong-wei ZHAO,Xiao-han LIU,Yuan ZHANG,Li-li FAN,Man-li LONG,Xue-bai ZANG. Clothing classification algorithm based on landmark attention and channel attention [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1765-1770.
[9] Hua CHEN,Wei GUO,Jing-wen YAN,Wen-hao ZHUO,Liang-bin WU. A new deep learning method for roads recognition from SAR images [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1778-1787.
[10] Qian XU,Ying LI,Gang WANG. Pedestrian-vehicle detection based on deep learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1661-1667.
[11] Li⁃min GUO,Xin CHEN,Tao CHEN. Radar signal modulation type recognition based on AlexNet model [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 1000-1008.
[12] LI Di-fei, TIAN Di, HU Xiong-wei. A method of deep learning based on distributed memory computing [J]. 吉林大学学报(工学版), 2015, 45(3): 921-925.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd