The increasing use of Machine Learning models in Cybersecurity applications has raised concerns about their interpretability and transparency. This research project aims to develop explainable Machine Learning-Based Intrusion Detection System (IDS) models and improve their performance.
This research project, funded by FCT - Foundation for Science and Technology UIDB/04524/2020, is developed by CIIC - Computer Science and Communication Research Centre - Polytechnic of Leiria.
- Datasets:
- Contains the datasets used for creating the Machine Learning IDS models.
- Pre-Modeling Phase:
- Includes notebooks and scripts for the pre-modeling phase.
- Exploratory Data Analysis (EDA): Notebooks for initial data exploration and visualization.
- Feature Understanding: Scripts for analyzing and understanding the features in the datasets.
- Feature Engineering: Notebooks for creating new features or modifying existing ones to improve model performance.
- Pre-Processing: Scripts for data cleaning, normalization, and preparation before modeling.
- Modeling Phase:
- Contains scripts and notebooks for the modeling phase.
- Model Creation: Scripts for training various Machine Learning models.
- Model Evaluation: Notebooks for assessing the performance of different models.
- Model Saving: Scripts for saving the trained models for future use.
- Explainable Phase:
- Includes notebooks with explainability libraries and tools.
- Feature Importance: Notebooks for determining the importance of each feature in the models.
- Individual Predictions: Tools for explaining specific predictions made by the models.
- Other Libraries: Additional libraries and methods for enhancing the interpretability of the models.
- Contributing: Contact the authors with questions or contributions: Ivo Bispo (
[email protected]).