Cyber Security Anomaly Detection System Data Mining and Machine Learning Course

A real-time anomaly detection system designed to identify cybersecurity threats in network traffic using a layered machine learning.

about Project This implements a multi-stage anomaly detection system to monitor network data for potential threats. It combines unsupervised learning techniques to detect anomalies and assess their severity, making it suitable for real-time cybersecurity applications.

Features

• Input Options: use cybersecurityanomalydetection dataset.

  1. Isolation Forest: Initial anomaly detection on full dataset.
  2. One-Class SVM: Refines anomalies from Isolation Forest.
  3. Autoencoder: Final severity scoring (Low/Medium/High) using a Deep Neural Network.

** Reach Me** @ wagarimisganu12@gmail.com github @ https://github.com/Wagarimisganu-github