AI-Based Operating System Security Upgrade Risk Assessment and Intelligent Decision-Making Model

As the complexity of network environments continues to increase, operating systems face significant security risks during the upgrade process. Traditional security risk assessment methods are no longer sufficient to meet the complex security demands, especially when dealing with large volumes of data and temporal information. This study proposes an AI-based operating system security upgrade risk assessment and intelligent decision-making model. The model integrates Long Short-Term Memory (LSTM) networks and Deep Neural Networks (DNN), enabling effective extraction of temporal features from system logs and user behavior data. It improves risk prediction accuracy through deep learning algorithms. Meanwhile, Reinforcement Learning (RL) is used to optimize the decision-making process and dynamically adjust upgrade strategies, maximizing the system's security and stability. Experimental results demonstrate that the proposed model outperforms traditional machine learning methods across multiple evaluation metrics, offering higher accuracy and adaptability. It is capable of effectively predicting potential risks during the operating system upgrade process and provides strong support for secure upgrade decision-making.