Virtual monitoring method for computer network security in earth observation communication using multi ant colony random walk classification algorithm

Traditional computer network security monitoring methods face problems such as relying on static rules, high false alarm rates, and lack of real-time performance. This study successfully implemented an efficient virtual monitoring method for computer network security by introducing a multi-ant colony random walk classification algorithm. Firstly, this article adopted multi-ant colony initialization. Each ant represents a monitoring node and collects network data comprehensively and efficiently through random walks. Secondly, ants randomly roam the network, collect real-time data, and extract key network traffic characteristics, such as traffic size, source, destination, etc. Through the collaborative work of multiple ant colonies, information sharing has been achieved and the ability to obtain global information in the network has been improved. This article combined machine learning classifiers and ant colony algorithms to establish a powerful monitoring system, successfully distinguishing different types of network traffic and improving network security. The computer network security virtual monitoring method based on a multi ant colony random walk classification algorithm performs well in performance evaluation. Based on the experimental results of the test set, this method has achieved high levels of precision (92%), recall (93.75%), accuracy (96.15%), and F1 Score (94.94%) performance indicators, successfully addressing the high false alarm rate and poor real-time performance of traditional methods.