Machine Learning for Anomaly Detection and Prediction in Network Data

Abstract

As network infrastructures expand in scale and complexity, the ability to detect anomalies and predict network disruptions has become a critical component of cybersecurity and operational reliability. This research investigates the application of machine learning techniques to improve anomaly detection and forecasting in network data. By leveraging a combination of supervised and unsupervised algorithms, our framework integrates clustering methods, support vector machines, and deep neural networks to identify irregular traffic patterns and anticipate potential failures. Experimental evaluations on multiple real-world datasets demonstrate that the proposed approach outperforms traditional rule-based systems in terms of detection accuracy and false-positive reduction. The model’s capacity to adapt to evolving network behaviors and its scalability in processing large volumes of data provide significant advantages for proactive network management. In addressing challenges such as feature selection, data imbalance, and computational overhead, our work emphasizes the importance of algorithmic tuning and robust preprocessing techniques. The predictive analytics component further enables network administrators to implement timely interventions, reducing downtime and mitigating risks associated with cyber threats. Overall, our findings suggest that integrating advanced machine learning methods into network monitoring systems offers a viable and effective solution for maintaining high levels of security and operational performance. This study lays the groundwork for future research aimed at enhancing anomaly detection and predictive capabilities in dynamic network environments while promoting a safer digital infrastructure. The promising results achieved underscore the potential of machine learning to revolutionize network security protocols, ensuring responses to emerging threats and reinforcing the stability of communication infrastructures.