This project focuses on intrusion detection using machine learning techniques on the NSL-KDD dataset. The dataset used for this project is the NSL-KDD dataset, which can be found here.
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Exploratory Data Analysis (EDA): Perform a comprehensive analysis of the dataset to understand the distribution, relationships, and characteristics of the features. This step helps in gaining insights into the data and identifying potential patterns.
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Data Preprocessing: Preprocess the data by handling missing values, scaling features, and encoding categorical variables. This step is crucial for preparing the data for model training.
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Model Building: Develop four Support Vector Machine (SVM) models with different kernel functions for intrusion detection. These models have achieved an accuracy of 93% on the NSL-KDD dataset. The SVM models will leverage the preprocessed data to make accurate predictions.
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data/
: Contains the dataset files used for the project. -
notebooks/
: Contains Jupyter notebooks documenting the EDA, data preprocessing, and model building steps. -
models/
: Contains the saved trained SVM models. -
src/
: Contains any additional source code or utility functions used in the project.