2024 Vol.7 Dec.N06

• Title: E-commerce Network Security Monitoring Based on HTM Algorithm and Random Forest
• Name: yaping Zhang
• Company: Wuhan Railway Vocational College Of Technology, Wuhan 430205,China
• Abstract:

  The conventional e-commerce network security monitoring methods mainly use MapReduce distributed processing technology to obtain potential threat characteristics of the network, which is easily affected by the driving effect of interoperability traceability, resulting in poor security monitoring effectiveness. Therefore, it is necessary to design a new e-commerce network security monitoring method based on HTM algorithm and random forest. The HTM algorithm was used to process the monitoring information of e-commerce network security, and a random forest framework for e-commerce network security monitoring was constructed, thus achieving e-commerce network security monitoring. The experimental results show that the designed HTM random forest monitoring method for e-commerce network security has high monitoring recall, accuracy, and precision, proving that the monitoring effect of the designed network security monitoring method is good, reliable, and has certain application value, making a certain contribution to reducing the operational security risks of e-commerce networks.

  
  

• Keyword: HTM algorithm; Random forest; Electronic Commerce; Network; Safety; monitor
• DOI: 10.12250/jpciams2024090709
• Citation form: yaping Zhang.E-commerce Network Security Monitoring Based on HTM Algorithm and Random Forest[J]. Computer Informatization and Mechanical System,2024,Vol.7,pp.37-39

[1].SHEN Z Q, MEI X J, ZHANG Y. Construction ideas and operational practices of provincial radio and television network intelligent terminal performance monitoring and security control platform [J]. Radio and Television Network, 2023, 30 (5): 97-100.

[2].DAI J R, CHEN D Q. Practice and exploration of systematic architecture for network security protection: A case study of hunan broadcasting and television station advertising management system [J]. Network Security Technology and Application, 2024, (1): 6-8.

[3].FAN C, HU G Q, DING T J, et al. Autonomous security analysis and penetration testing model based on attack graph and deep Q-learning network [J]. Chinese Journal of Network and Information Security, 2023, 9 (6): 166-175.

[4].SONG J. Research on network security monitoring and early warning based on cloud computing and data mining technology [J]. Information Systems Engineering, 2023, (10): 138-141.

[5].WANG N. Research on network data security monitoring and management based on big data technology [J]. Somputer Engineering & Software, 2023, 44 (9): 145-147.

[6].JIANG Z B, LI S, LIU Y Y. Network security analysis method for medical devices under medical health scenarios based on Microsoft threat modeling tool [J]. China Medical Devices, 2023, 38 (12): 113-118.