hectorpadin1/Network-Intrusion-Detection-System
En este proyecto se evalúan y comparan diferentes técnicas de aprendizaje automático para la detección de intrusiones en red.
This project helps cybersecurity professionals and network administrators improve their ability to detect intrusions in their network traffic. It takes raw network flow data, like Netflow or IPFIX records, and applies various machine learning techniques to identify both known attack patterns and unusual, anomalous network behaviors. The output helps identify potential cyber threats, allowing for quicker response and mitigation.
No commits in the last 6 months.
Use this if you need to evaluate and compare different machine learning strategies to enhance your Network Intrusion Detection System (NIDS) and are working with labeled network flow datasets.
Not ideal if you are looking for a ready-to-deploy, production-grade NIDS solution, as this project focuses on the evaluation and comparison of detection techniques.
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28
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7
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Nov 01, 2022
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