Application Analysis of Network Security Situational Awareness Model for Asset Information Protection

The popularity of the Internet makes the network develop rapidly. However, the network security threat is more complex and hidden. The traditional network security alarm system has the problems of low accuracy and low efficiency when dealing with huge redundant data. Therefore, the research comprehensively considers the network security problems, proposes a network security situational awareness model for asset information protection combined with knowledge graph, establishes an asset-based network security knowledge graph, utilizes attribute graphs to complete the network attack scenario discovery and network situational understanding, and verifies the effectiveness and superiority of the model. The experimental results show that the research-proposed model detects an average of 9706 attacks out of 10000 attacks. For 100 high-risk level attacks, the number of detections is higher than 98. The average correctness, recall, and false alarm rates of the research proposed model are 99.48%, 99.04%, and 0.86%, respectively. In addition, when the model is running, its maximum memory usage is only 22.67%, and the time to complete the attack detection at the same time is 258.4s, both of which are much lower than the comparison algorithms. Finally, the research-proposed model is able to effectively reflect the impact of attack events on the posture of asset nodes. The proposed cybersecurity situational awareness model is of great theoretical and practical significance for improving organizational cybersecurity, innovating cybersecurity solutions, and maintaining the security of asset information in the digital era.

@article{Ren2024,
  title     = {Application Analysis of Network Security Situational Awareness Model for Asset Information Protection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {5},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yuemei Ren and Xianju Feng},
  doi       = {10.14569/IJACSA.2024.0150579},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150579}
}