Embark on a cybersecurity journey with our PyTorch-based neural network classifier, specifically designed for NSL-KDD data. This project focuses on the careful preparation of NSL-KDD data, including normalization of numerical attributes and one-hot encoding of categorical attributes.
Key Features:
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Data Preparation: Streamline the preprocessing of NSL-KDD data by normalizing numerical attributes and implementing one-hot encoding for categorical attributes.
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Neural Network Design: Delve into the core of the project with a PyTorch-based neural network that emphasizes the construction of encoders and decoders.
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Hyper-Parameter Optimization: Model performance was enhanced by fine-tuning hyper-parameters, such as the learning rate and reconstruction error threshold.
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Performance Evaluation: The neural network attains a 89% accuracy on the NSL-KDD validation dataset.
Browse the repository to examine the code.