Deep Learning Driven Approaches for Real-Time Anomaly Detection in High-Velocity Network Traffic

Karl May Gotthold

Deep Learning Driven Approaches for Real-Time Anomaly Detection in High-Velocity Network Traffic

Authors

Karl May Gotthold

Network Analyst, Spain.

Keywords:

Network Anomaly Detection, Deep Learning, Real-Time Monitoring, Cybersecurity, High-Velocity Traffic, Edge Computing, Transformer Models

Synopsis

The proliferation of high-speed digital communication and evolving cyber threats has propelled the need for intelligent, scalable, and real-time anomaly detection systems. Traditional machine learning models often fail to adapt to dynamic, high-velocity environments with large-scale data. This paper explores recent advances in deep learning models tailored for real-time anomaly detection in high-velocity network traffic. Key architectures such as CNN-LSTM, Transformer networks, and AutoEncoders are evaluated based on accuracy, scalability, and latency. The integration of these models with edge computing and federated learning offers further potential to enhance response times and privacy. Our analysis reflects the current landscape , revealing a strong shift toward hybrid, explainable, and low-latency models capable of autonomous learning.

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