https://github.com/namratha2301/intrusiondetection

Intrustion Detection Models based on Internet Traffic Data obtained from the NSL-KDD Dataset
https://github.com/namratha2301/intrusiondetection

decisiontree gradient-boosting intrusion-detection mlp-classifier naive-bayes-classifier nsl-kdd randomforest scikit-learn

Last synced: 29 days ago
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Intrustion Detection Models based on Internet Traffic Data obtained from the NSL-KDD Dataset

• Host: GitHub
• URL: https://github.com/namratha2301/intrusiondetection
• Owner: Namratha2301
• License: mit
• Created: 2022-10-24T05:47:38.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-10-29T07:54:29.000Z (about 2 years ago)
• Last Synced: 2023-03-11T23:23:33.934Z (almost 2 years ago)
• Topics: decisiontree, gradient-boosting, intrusion-detection, mlp-classifier, naive-bayes-classifier, nsl-kdd, randomforest, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 964 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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About the Project

The project uses the NSL-KDD Dataset from Kaggle to create 

machine learning models that allow the intrusion detection in networks based on the Internet traffic info.The dataset was downloaded from Kaggle. 

Here is the  link to the dataset.


Setup


To run the notebook one can either prefer using Google Colab the better method or run the notebook locally.

For running the notebook locally, follow the steps [Windows]:

1. Clone the repository using `git clone https://github.com/Namratha2301/IntrusionDetection.git`

2. Set directory to cloned repo `cd IntrusionDetection`

3. Create a python virtual environment for the project using `python -m venv env`

4. Activate the environment using `env\Scripts\activate`

5. Install the dependencies using `pip install -r requirements.txt`

6. Open the Jupyter Notebook IDE using `jupyter notebook`

7. The Jupyter Notebook IDE should open up allowing you to run the file

Machine Learning Models and Scores


S.No

Model

Package

Score

1

Random Forest 

SciKit-Learn

99.5%

2

Support Vector Machine

SciKit-Learn

98.2%

3

Logistic Regression 

SciKit-Learn

93.7%

4

Gaussian Naive Bayes

SciKit-Learn

88%

5

Gradient Boosting

SciKit-Learn

99.1%

6

Multi-Layer Perceptron

SciKit-Learn

99.2%

7

Decision Tree

SciKit-Learn

92.4%