Information Network Security Situation Awareness Based on Artificial Intelligence and Machine Learning Algorithms

In the face of increasingly complex network environments, this study aims to improve the ability of information network security situation awareness. Through artificial intelligence and machine learning technologies based on the isolation forest (IF) algorithm, a multi-level and modular security situation awareness framework is constructed. The framework integrates deep learning, natural language processing and adaptive learning technologies to perform indepth analysis and feature extraction on multi-source data to improve the detection, identification and response capabilities of network threats. Experimental results show that the framework exhibits high detection accuracy and an average response time of less than 100 seconds in standardized tests, and effectively responds to emergencies through adaptive learning modules. Multiple simulated network attack scenarios verify the performance and reliability of the framework under different conditions. The research results establish the effectiveness of the isolation forest algorithm in information network security and show advantages in key indicators such as false alarm rate, accuracy, detection delay and true negative rate. Future research will continue to explore the integration of multi-source information to improve recognition performance in complex network environments.