Early Anomaly Detection in Network Traffic Using Deep Learning Techniques Based on NetFlow Data

Traditionally, intrusion detection systems have proven unreliable when detecting stealthy or low-rate types of attacks. Increasing numbers of cyber threats have accelerated the need for better ways to monitor network activity. In this paper, we discuss the advantages of analyzing NetFlow data in order to detect intrusion anomalies without packet-level analysis. The methodology we propose is based on deep learning and NetFlow data sets, applied to detect anomalies in a given network environment. We will perform preprocessing on the flow data set and extract the relevant attributes using Autoencoder and LSTM networks. Finally, our findings reveal that this methodology exceeds performance through its enhanced ability to detect subtle attacks. The framework offers a scalable and efficient solution for improving real-time network security.