kahramankostas/Anomaly-Detection-in-Networks-Using-Machine-Learning
A thesis submitted for the degree of Master of Science in Computer Networks and Security
This project helps network security professionals and researchers analyze network traffic data to detect anomalies and potential attacks. You feed it raw network flow data (like the CIC-IDS2017 dataset), and it processes this information to identify distinct attack types and benign traffic. The output includes statistical summaries, filtered attack data, key features for detection, and performance metrics from various machine learning models trained to spot these anomalies.
237 stars. No commits in the last 6 months.
Use this if you need to systematically process network traffic data to train and evaluate machine learning models for identifying different kinds of network attacks.
Not ideal if you're looking for a real-time anomaly detection system or a tool to directly implement into an operational network environment.
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Nov 16, 2022
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