A Comprehensive Guide to Building an Intrusion Detection System Using Machine Learning in Python
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This tutorial will guide you through the entire process of building an Intrusion Detection System (IDS) using machine learning.
We’ll use Python, the KDD Cup 1999 dataset, and a Random Forest Classifier to detect potential cyber threats. By the end of this guide, you’ll have a functional IDS that can identify anomalous network activity.
1. Prerequisites
Before we dive into the implementation, ensure you have the following:
- Python 3.8 or above installed.
- A code editor or IDE (e.g., Visual Studio Code, Jupyter Notebook, or PyCharm).
- Basic understanding of Python and machine learning concepts.
2. Installing Dependencies
You need the following Python libraries:
- pandas for data manipulation.
- scikit-learn for machine learning algorithms.
- numpy for numerical operations.
Run the following command to install them:
pip install pandas scikit-learn numpy
