The Impact Of AI-assisted Anomaly Detection On Continuous Network Monitoring

Authors: Divya Pillai

Abstract: The rapid growth of digital networks and the proliferation of cyber threats have underscored the critical importance of continuous network monitoring to ensure security, reliability, and optimal performance. Artificial intelligence (AI)-assisted anomaly detection has emerged as a transformative approach, profoundly enhancing the capabilities of network monitoring systems. This article explores the impact of AI-assisted anomaly detection on continuous network monitoring, discussing how advanced algorithms and machine learning techniques detect unusual patterns, identify potential threats, and enable proactive responses. By integrating AI, network administrators can achieve real-time detection with higher accuracy, reducing false positives and enabling a more efficient allocation of resources. This article examines the fundamental principles of AI in anomaly detection, its implementation challenges, and the benefits it brings to modern network environments. It also covers use cases spanning various industries, emphasizing how AI fosters adaptive security measures in an ever-evolving threat landscape. Finally, it highlights future trends and potential developments that could further revolutionize network monitoring. The analysis provides in-depth insights into how AI-driven anomaly detection is shaping the future of network management and cybersecurity.

DOI: http://doi.org/10.5281/zenodo.17709759