Western-OC2-Lab / Intrusion-Detection-System-Using-Machine-Learning
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
Why this job: works in the areas we cover, but does not say which job it does
What it does
This repository contains the code for the project "IDS-ML: Intrusion Detection System Development Using Machine Learning". The code and proposed Intrusion Detection System (IDSs) are general models that can be used in any IDS and anomaly detection applications. In this project, three papers have been published:
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Inactive
- Last commit 7 months ago
- Stars
- 602
- +1 this week
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- Jupyter Notebook
- 100% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 100% closed
- 0 still open
- Pull requests
- 0 merged
- 1 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Jupyter Notebook 100%
Topics
- machine-learning
- random-forest
- decision-tree
- xgboost
- bayesian-optimization
- hyperparameter-optimization
- hpo
- kmeans
- python-examples
- intrusion-detection
- intrusion-detection-system
- autonomous-vehicles
Project record
- JobOther AI tools
- StatusListed
- Commits52
- Forks162
- Watching11
- Stars in 28 days+5
- CreatedMay 25, 2021
- First seen by usNov 7, 2026
- CheckedNov 8, 2026