Network Information Transmission Security Situation Awareness Algorithm on Basis of Data Analysis

Network Information Transmission Security Situation Awareness Algorithm on Basis of Data Analysis

연구 분야: Networking

학회: 2024 International Conference on Data Science and Network Security (ICDSNS)

In the context of big data, existing network security situational awareness models have shortcomings such as high resource consumption, low analysis accuracy, low processing efficiency, and inability to adapt to large-scale scenarios. This article aimed to improve the security and resilience of the network through real-time monitoring, analysis, and identification of security events and threats in the network through network information transmission security situational awareness algorithms. This article analyzed the Locality Sensitive Hashing (LSH) algorithm for network information transmission security situational awareness. If certain values appear abnormally frequently or are related to known malicious behavior, they can be marked as potential security threats and corresponding defense measures can be taken. The findings indicate that the LSH algorithm's detection rate range was between 90%-95%, while the Bayesian algorithm's detection rate range was between 80% -85%. The security and dependability of network information transmission can be increased by using the LSH algorithm to assist network security teams in quickly identifying and addressing a variety of network security issues.