sinanw/ml-classification-malicious-network-traffic

This project aims to analyze and classify a real network traffic dataset to detect malicious/benign traffic records. It compares and tunes the performance of several Machine Learning algorithms to maintain the highest accuracy and lowest False Positive/Negative rates.

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This project helps cybersecurity analysts detect malicious activity by classifying network traffic. It takes raw network traffic data, processes it, and then uses machine learning to identify whether connections are benign or malicious. The output is a clear classification of traffic, which is useful for security operations teams or network administrators.

No commits in the last 6 months.

Use this if you need to automatically detect and classify malicious network traffic using machine learning techniques to improve your network security posture.

Not ideal if you are looking for a real-time intrusion prevention system rather than a framework for analyzing historical network traffic data.

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

Stale 6m No Package No Dependents

Maintenance 0 / 25

Adoption 6 / 25

Maturity 16 / 25

Community 15 / 25

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Jupyter Notebook

License

MIT

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