Research on Network Security Situation Prediction Method based on Deep Reinforcement Learning

Abstract

The effort categorically introduces a novel paradigm of deep reinforcement learning algorithm for predicting cybersecurity situations which balances the needs for accuracy, timeliness, and adaptability in detecting threats in cyberspace. The framework combines the latest in deep learning technologies with effective reward systems and strategies for policy optimisation, resulting in increased prediction capabilities. The model achieved 94.3% prediction accuracy across 13 varieties of scenarios, which is 15.2% higher than traditional methods. Comprehensive experimental verification has distinguished the model as supremely accurate in nearly real-time prediction tasks. Such a feat places the model at the forefront in usability theoretically and practically. Its dual stream feature extraction and policy learning network enhances the efficiency of complex security pattern recognition. As such, the framework exhibits great robustness with enhanced repeatability, culminating in a 35% average response time advantage over traditional strategies. This research supports the improvement of cybersecurity capabilities by a prediction model of flexible security situation changes that is reliable, and was verified with in-depth comparative and real-world scenario testing.