Detecting network intrusion using various machine learning algorithms. Monitor a network or system for malicious activity and protects a computer network from unauthorized access from users, including perhaps insider. The intrusion detector learning task is to build a predictive model (i.e. a classifier) capable of distinguishing between ‘bad connections’ (intrusion/attacks) and a ‘good (normal) connections’.
The data used here comes up with no 100% guarantee. So, don't use it for making decisions. However, this project presents the idea that how we can use MACHINE LEARNING in tackling Cyber Security Threats and can save our systems from being corrupted.
Cyber Security is very important these days as everyone has systems and everyone is using internet and its services to do their work. In this kind of situations it is very important that we learn about some measures to tackle these threats.
git clone
Install the required dependencies using pip: pip install -r requirements.txt
Run the Jupyter Notebook or Python scripts to train models and make predictions.
The dataset used in this project is taken from Kaggle : https://www.kaggle.com/datasets/sampadab17/network-intrusion-detection
1.Logistic Regression 2.Decision Tree Classifier 3.K Neighbour Classifier
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If you would like to contribute to any of my projects, please fork this repository and create a new branch for your changes. Once you are finished, please submit a pull request.
1.Fork the repository. 2.Create a new branch for your feature or bug fix. 3.Make your changes and commit them. 4.Push to your fork and submit a pull request to the main repository.