George9822/CICIDS_2017and2018_IntrusionDetectionSystem

This project aims to identify and classify the anomalies captured in network traffic using different machine learning strategies. After the reults are given, I compared the results of two classical approaches for supervised learning: RandomForest and SVM on a large public combined dataset made from CICIDS2017 dataset and CICIDS2018.

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Experimental

This project helps network security analysts detect and classify unusual or malicious activity in their network traffic. By analyzing combined network flow data from the CICIDS2017 and CICIDS2018 datasets, it identifies potential intrusions. The output is a classification of these anomalies, helping network defenders understand and respond to threats.

No commits in the last 6 months.

Use this if you are a network security professional looking to understand machine learning approaches for identifying network intrusions based on known datasets.

Not ideal if you need a plug-and-play intrusion detection system for real-time deployment in a live network environment.

network-security intrusion-detection cybersecurity-analysis network-traffic-monitoring threat-detection
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 11 / 25

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Last pushed

Jul 14, 2022

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