Remote Data Security Monitoring Technology for Computer Networks Based on Machine Learning Algorithms

In an era dominated by increasing cyber threats and the expansion of digital systems, securing computer networks has become a critical challenge. Traditional security techniques, such as firewalls and intrusion detection systems, are often inadequate in addressing the dynamic and evolving nature of modern cyber-attacks. Remote data security monitoring, when combined with machine learning (ML) algorithms, has emerged as a promising solution to address these security gaps. This paper explores the integration of machine learning techniques into remote data security monitoring systems for computer networks. It investigates how ML algorithms can enhance real-time detection, classification, and prediction of security incidents, thereby improving the robustness and efficiency of network defense mechanisms. The study reviews various ML models, their application to data security, and the benefits and challenges of their deployment in remote monitoring systems. Finally, we present case studies and discuss future research directions in this domain.