Enhanced AI-Driven Techniques for Predictive Network Traffic Anomaly Detection and Mitigation Strategies

Abstract

The rapid increase in cyber threats has necessitated the development of more robust methods for network security. This paper explores enhanced AI-driven techniques for predictive network traffic anomaly detection and the corresponding mitigation strategies. By leveraging machine learning algorithms and deep learning architectures, the study aims to identify patterns and anomalies in network traffic effectively. The paper discusses various methodologies, performance metrics, and case studies that highlight the efficiency of these techniques in real-world applications, ultimately contributing to a proactive cybersecurity framework.