Network anomaly detection and security defense technology based on machine learning: A review

Robust solutions are essential for protecting complex network systems in the constantly changing cybersecurity scenario. This investigation examines the role of machine learning (ML) in improving the safety of digital infrastructure by examining network anomaly detection and security defense. We evaluate the effectiveness of key ML techniques, including deep learning (DL), ensemble methods, and supervised and unsupervised learning, in recognizing abnormalities and reducing dangers. Real-time monitoring and adaptive reactions are emphasized in the analysis of ML-based systems integration into comprehensive security frameworks. This section also addresses challenges like interpretability, harmful assaults, and model resilience. Real-world case examples emphasize the importance of ML in the advancement of network security. The paper continues with a discussion of ML's future role in the optimization of security measures and the mitigation of emergent threats.