Computer Python Network Security Monitoring System Based on Intelligent Algorithms

In response to the increasingly complex and diverse network security threats, a network security monitoring system is introduced that combines intelligent algorithms and Python software to achieve efficient and accurate real-time security monitoring and protection. Firstly, the system collects network traffic and log data through libraries such as Scapy and Pyshark, and performs preprocessing. Then, using Python's machine learning library Scikit learn, features are extracted, including traffic patterns, protocol types, source IP (Internet Protocol) addresses, etc., to construct a classification model. Next, based on the characteristics of network attacks, the random forest algorithm in supervised learning is selected to train the annotated attack samples. The false alarm rate of the system is 3.1%, indicating that when detecting the spread of malicious software, the system occasionally misjudges normal file transfers as malicious behavior. The system has significantly improved the efficiency and accuracy of network security monitoring through the application of intelligent algorithms, and has broad application prospects.