Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 241100095-6.doi: 10.11896/jsjkx.241100095

• Information Security • Previous Articles     Next Articles

Research on Security Performance Evaluation and Verification of Video Surveillance NetworkBased on Variable Fuzzy Theory

WANG Keke1, BIAN Yue1, YIN Yanyan2   

  1. 1 China Aerospace Academy of Systems Science and Engineering,Beijing 100037,China
    2 Beijing Normal University-Hong Kong Baptist University United International College,Zhuhai,Guangdong 519087,China
  • Online:2025-11-15 Published:2025-11-10
  • About author:WANG Keke,born in 1986,master,se-nior engineer,is a senior member of CCF(No.C3050S).His main research interests include risk assessment,scripting language security,virus,multimedia security,cloud computing system security and systems engineering.

Abstract: At present,research on the security of video surveillance network systems still remains at the level of security indicator systems and indicator weights.In order to further carry out research on the security of video surveillance network systems,a variable fuzzy evaluation model was established to evaluate the security performance of video surveillance network systems.Regret theory was introduced to verify the evaluation results,and actual cases were used to verify the effectiveness of the evaluation model.This study uses variable fuzzy theory to calculate the comprehensive relative membership degrees of fuzzy evaluation mo-del,neuron excitation function model,TOPSIS ideal point model,and fuzzy optimization evaluation model.Then,the evaluation results corresponding to these four models are obtained,and their arithmetic mean is calculated to obtain the final evaluation level.The verification method based on regret theory used in this study can select the optimal evaluation scheme,and thus verify the evaluation results using the variable fuzzy evaluation model.The evaluation model proposed in this study helps to take targeted measures for weak links in video surveillance networks,improve the overall security of video surveillance network systems,and provide guarantees for the security construction and use of video surveillance systems.

Key words: Variable fuzzy theory, Video, Surveillance, Network, Security, Evaluation, Regret theory, Verification

CLC Number: 

  • TP393.1
[1]YU C J.On the Security Construction of Public Security Video Network from the Angle of Graded Protection[J].China Public Security,2018(7):178-179.
[2]FANG X,WANG H,XU X L,et.al.On the Security Construction of Network Video Monitoring System in Sensitive Information Places[J].Network Security Technology AND Application,2024(6):142-144.
[3]ZOU T,TAO S,DONG F.Analysis on Security Risk Prevention and Governance of Network Video Monitoring System under the Background of Metauniverse[J].Computer Knowledge and Technology,2024,20(4):96-99,102.
[4]ZHOU S H,TANG Y,YANG Q R,et al.A Security Evaluation Method of Video Surveillance System Based on Network Anti kill Chain[J].Security Science and Technology,2023(9):49-56.
[5]XU Y Z.Network address planning of public security video surveillance project[J].Radio & Television Network,2023,30(8):56-58.
[6]LIU P H.Design and Implementation of Video Surveillance Network Device Identification and Security Monitoring System[D].Beijing:Beijing University of Posts and Telecommunications,2023.
[7]XU H.Research on Cyber-security of Railway Integrated Video Monitoring System[J].Railway Signalling & Communication Engineering,2023,20(5):39-43.
[8]GAO J,WANG K Y,HUANG S H.Research on Security Evaluation Index System for Video Monitoring Network[J].Netinfo Security,2021,21(12):78-85.
[9]ZHU L N,HUANG H B,ZHANG X F,et al.Analysis on Network Security Risks and Protective Measures of Industrial Intelligent Video Monitoring System[J].Security Science and Technology,2021(11):25-31.
[10]WANG C L,GAO Q.Dynamic Evaluation Method of Multi-Video Surveillance Network Security Risk for Police Equipment[J].Journal of Ordnance Equipment Engineering,2021,42(10):218-223.
[11]WANG T.Research on Current Situation and Security Problems of Network Video Monitoring System[J].Network Security Technology AND Application,2020(9):146-147.
[12]YUAN D Y,HUANG S H,GAO J.Research on Current Situation and Countermeasures of Video Surveillance Network Security Based on Scanning Analysis[J].Science and Technology Management Research,2021,41(4):198-204.
[13]YANG L M.Analysis of data security governance of public videomonitoring network[J].China Security & Protection,2020(7):10-13.
[14]XUE Y P.Design of Network Video Monitoring System Based on Embedded System and Network Information Security[J].China Computer & Communication,2019(12):78-79.
[15]LIU Y F.The Research on Network Security Access Protocolfor Power System Video Surveillance[D].Xi’an:Xidian University,2019.
[16]CUI E Z.Application of information technology in network videomonitoring of refinery safety production[J].Digital Space,2018(4):247.
[17]LI X K,LI B,SHI Y B,et al.Research on Wireless Video Monitoring System Technology for Safe Production under 4G Network[J].Computer Products and Circulation,2017(8):8.
[18]CHEN S Y.Theory and Model of Variable Fuzzy Sets and Its Application[M].Dalian:Dalian University of Technology Press,2009.
[19]BELL D E.Regret in Decision Making under Uncertainty[J].Operations Research,1982,30.
[20]GREEN S L.Rational choice theory:An overview[J/OL].http://business.baylor.edu/steve_green/green1.doc.
[21]YAO Y.A Evaluation Method Based on Regret Theory andGrey Entropy TOPSIS[J].Journal of Mathematics in Practice and Theory,2024(10):121-129.
[22]TVERSKY A,KAHNEMAN D.Advances in Prospect Theory:Cumulative Representation of Uncertainty[J].Journal of Risk and Uncertainty,1992,5(4):297-323.
[1] WANG Jinghong, LI Pengchao, WANG Xizhao, ZHANG Zili. Dual-channel Graph Neural Network Based on KAN [J]. Computer Science, 2026, 53(3): 188-196.
[2] WANG Yizong, NING Hongbo, WANG Haofeng, MA Siwei, GAO Wen. Low-bitrate and Real-time Multiview Video Streaming with 3D Gaussian Splatting [J]. Computer Science, 2026, 53(3): 225-230.
[3] DU Jiantong, GUAN Zeli, XUE Zhe. Multi-task Learning-based Ophthalmic Video Feature Fusion and Multi-dimensional Profiling [J]. Computer Science, 2026, 53(3): 383-391.
[4] YANG Ruoxuan, JIN Feiyu, QU Lianwei, ZHOU Zijie, ZHENG Qibin, LI Zhenhua. A Serverless-based Approach to Fast Measurement of Network Bandwidth [J]. Computer Science, 2026, 53(3): 392-399.
[5] ZHANG Xinfan, CHENG Baolei, FAN Jianxi, WANG Yan. Connectivity and Diagnosability of Data Center Network SWCube [J]. Computer Science, 2026, 53(3): 400-410.
[6] CHANG Huiyan, HU Hongchao, ZHOU Dacheng, XU Depeng, CHENG Guozhen. Research Review of Application-based Covert Channel [J]. Computer Science, 2026, 53(3): 411-423.
[7] SONG Jianhua, HE Jiawei, ZHANG Yan. Dual-channel Source Code Vulnerability Detection Model Based on Contrastive Learning [J]. Computer Science, 2026, 53(3): 424-432.
[8] DING Yan, DING Hongfa, YU Muran, JIANG Heling. Survey of Backdoor Attacks and Defenses on Graph Neural Network [J]. Computer Science, 2026, 53(3): 1-22.
[9] CHEN Han, XU Zefeng, JIANG Jiu, FAN Fan, ZHANG Junjian, HE Chu, WANG Wenwei. Large Language Model and Deep Network Based Cognitive Assessment Automatic Diagnosis [J]. Computer Science, 2026, 53(3): 41-51.
[10] ZHAO Zhengbiao, LU Hanyu, DING Hongfa. Node-influence Based Construction Algorithm of Approximate Worst-case Forgetting Set for Graph Unlearning [J]. Computer Science, 2026, 53(3): 64-77.
[11] LI Zequn, DING Fei. Fatigue Driving Detection Based on Dual-branch Fusion and Segmented Domain AdaptationTransfer Learning [J]. Computer Science, 2026, 53(3): 78-87.
[12] CUI Mengtian, HE Liwen, XIE Qi, WANG Fang. Group Semantic-driven Hypergraph Network for Disinformation Detection with Fusion PropagationStructure [J]. Computer Science, 2026, 53(3): 88-96.
[13] LIU Huashuai, TAO Houguo, YUE Kun, DUAN Liang. Bayesian Network Based Fault Root Cause Analysis [J]. Computer Science, 2026, 53(3): 143-150.
[14] CHANG Xuanwei, DUAN Liguo, CHEN Jiahao, CUI Juanjuan, LI Aiping. Method for Span-level Sentiment Triplet Extraction by Deeply Integrating Syntactic and Semantic
Features
[J]. Computer Science, 2026, 53(2): 322-330.
[15] LI Fang, YUAN Baochun, SHEN Hang, WANG Tianjing, BAI Guangwei. Deep Reinforcement Learning-based Aircraft Task Offloading in Low Earth Orbit Satellite Networks [J]. Computer Science, 2026, 53(2): 406-415.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!