abhinav-bhardwaj/Network-Intrusion-Detection-Using-Machine-Learning
A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach
This project helps cybersecurity analysts and network administrators automatically identify and classify network intrusions. It takes raw network traffic data and outputs classifications indicating whether traffic is normal or malicious (e.g., Denial of Service, Probe, R2L, U2R). The end-user is a security professional responsible for safeguarding network integrity.
145 stars. No commits in the last 6 months.
Use this if you need to build or evaluate a machine learning model to detect network intrusions from traffic data, providing both binary (attack/no attack) and multi-class (specific attack types) classifications.
Not ideal if you're looking for a plug-and-play intrusion detection system that integrates directly into a live network environment without requiring any coding or data science expertise.
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145
Forks
53
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Oct 12, 2021
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