Real-time network traffic anomaly detection is crucial for the confidentiality, integrity, and security of network information. Machine learning approaches are widely used to distinguish traffic flow ...

Cluster Profiling: Characterizes each cluster to understand what type of network behavior it represents (e.g., normal traffic, potential attacks, data exfiltration). Visualization : Employs Principal ...

This project focuses on detecting anomalies in network traffic using clustering techniques (K-means and hierarchical clustering) on unlabeled data. The KDD Cup 1999 dataset is utilized to identify ...

Real-time network traffic anomaly detection is crucial for the confidentiality, integrity, and security of network information. Machine learning approaches are widely used to distinguish traffic flow ...