This project aims to develop a machine learning model for detecting network intrusions using the NSL-KDD dataset. The model classifies network traffic into two categories: benign (normal) and malicious (various types of attacks). By leveraging a neural network architecture, this project demonstrates the potential of machine learning in enhancing cybersecurity.
The NSL-KDD dataset is a widely used benchmark for evaluating intrusion detection systems. It contains a variety of network traffic data, including both normal and attack instances.
- Data preprocessing, including encoding categorical features and standardizing numerical values.
- Implementation of a neural network model using TensorFlow and Keras.
- Evaluation of model performance using metrics such as accuracy, precision, recall, and F1-score.
- Visualization of results through confusion matrix and classification reports.
To run this project, you will need to have Python installed along with the following libraries:
- numpy
- pandas
- tensorflow
- scikit-learn
- matplotlib
- seaborn
You can install the required libraries using pip:
pip install numpy pandas tensorflow scikit-learn matplotlib seaborn