elifnurkarakoc/CICIDS2017

CICIDS2017 dataset

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This project helps cybersecurity analysts and network security engineers evaluate the performance of different machine learning models for detecting network intrusions. You provide a prepared network traffic dataset, and it processes this data to train and test various models, outputting their accuracy, precision, recall, and F1-score to help you understand their effectiveness in identifying different types of attacks.

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Use this if you need to systematically assess and compare how well different machine learning models can identify cyberattacks within a network traffic dataset.

Not ideal if you're looking for a tool to perform real-time intrusion detection or if you need to build and deploy a production-ready security system.

network-security cybersecurity-analytics intrusion-detection security-research threat-analysis

No License Stale 6m No Package No Dependents

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Adoption 9 / 25

Maturity 8 / 25

Community 19 / 25

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