The Deep Learning-Based Security Assessment and Optimization Model for Enterprise Information Systems Under Digital Economy

Abstract

With the increasing complexity of enterprise systems and the rise in cyber threats, managing security risks while optimizing resources has become a significant challenge. Traditional models often address security and resource management in isolation, making it difficult to adapt to evolving threats and dynamic workloads. This paper proposes the deep learning-based dynamic security assessment and optimization model, which integrates dynamic security assessment, anomaly detection, multi-modal data fusion, security investment optimization, and cloud resource optimization into a unified framework. By leveraging deep learning techniques such as convolutional neural networks for feature extraction and recurrent neural networks for temporal anomaly detection, alongside reinforcement learning for resource optimization, the deep learning-based dynamic security assessment and optimization model provides real-time risk evaluation and adapts resource allocation based on system needs.