Design and Implementation of Computer Network Security Monitoring System
DOI:
https://doi.org/10.71222/qkantx75Keywords:
network security, monitoring system, threat detection, data collection, visualizationAbstract
With the increasing complexity of computer network security issues, traditional security protection methods are unable to meet the needs of dynamic network environments. In response to this challenge, this article has developed an innovative computer network security monitoring system. The system adopts a modular architecture and has key functions such as real-time monitoring, threat analysis, and automatic response. It mainly consists of network data collection unit, data preprocessing and storage module, threat detection, and security event response mechanism, effectively detecting and processing various potential risks. The system integrates multi-faceted security visualization technology, presenting clear security event analysis results and log review functions to users. The experimental results show that the system exhibits outstanding advantages in data traffic monitoring, attack tracing, risk assessment, and security policy implementation, providing a solid technical foundation for addressing security challenges in complex network environments.
References
1. L. Tan, K. Yu, F. Ming, X. Cheng, and G. Srivastava, “Secure and resilient artificial intelligence of things: a HoneyNet approach for threat detection and situational awareness,” IEEE Consum. Electron. Mag., vol. 11, no. 3, pp. 69–78, May 2021, doi: 10.1109/MCE.2021.3081874.
2. G. Engelen, V. Rimmer, and W. Joosen, “Troubleshooting an intrusion detection dataset: the CICIDS2017 case study,” in Proc. IEEE Secur. Privacy Workshops (SPW), May 2021, pp. 7–12, doi: 10.1109/SPW53761.2021.00009.
3. T. M. Booij, I. Chiscop, E. Meeuwissen, N. Moustafa, and F. T. Den Hartog, “ToN_IoT: The role of heterogeneity and the need for standardization of features and attack types in IoT network intrusion data sets,” IEEE Internet Things J., vol. 9, no. 1, pp. 485–496, Jan. 2022, doi: 10.1109/JIOT.2021.3085194.
4. T. Saba, A. Rehman, T. Sadad, H. Kolivand, and S. A. Bahaj, “Anomaly-based intrusion detection system for IoT networks through deep learning model,” Comput. Electr. Eng., vol. 99, p. 107810, Jan. 2022, doi: 10.1016/j.compeleceng.2022.107810.
5. K. Yu, L. Tan, S. Mumtaz, S. Al-Rubaye, A. Al-Dulaimi, and A. K. Bashir et al., “Securing critical infrastructures: Deep-learning-based threat detection in IIoT,” IEEE Commun. Mag., vol. 59, no. 10, pp. 76–82, Oct. 2021, doi: 10.1109/MCOM.101.2001126.
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Copyright (c) 2025 Yihong Zou (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
