{"id":29748759,"url":"https://github.com/achrefbenammar404/ai4cybersecworkshop","last_synced_at":"2025-10-28T02:31:59.293Z","repository":{"id":258426274,"uuid":"873875345","full_name":"achrefbenammar404/AI4CyberSecWorkshop","owner":"achrefbenammar404","description":"This repository contains the materials for the AI in Cybersecurity workshop organized in collaboration with IEEE ENICarthage University. 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This workshop was organized in collaboration with **IEEE ENICarthage University** to introduce participants to the role of **Artificial Intelligence (AI)** in **Cybersecurity**. This repository contains the materials, code, datasets, and presentation slides used during the workshop.\n\u003cdiv style=\"text-align: center;\"\u003e\n    \u003cimg src=\"resources/1.jpg\" alt=\"FirstSlide\" width=\"800\"/\u003e\n\u003c/div\u003e\n\n\n---\n\n## Workshop Overview\n\nThe **AI in Cybersecurity Workshop** is designed to help participants understand how AI can be leveraged to protect systems from cyber threats. The workshop focuses on two key cybersecurity problems:\n1. **Malware Detection**: Using AI to detect malicious software.\n2. **Time to Compromise (TTC) Prediction**: Predicting the time it takes for an attacker to breach a system based on system vulnerabilities.\n\n### Key Concepts Covered:\n- Introduction to Artificial Intelligence and Machine Learning.\n- Understanding classification and regression problems.\n- Building and evaluating machine learning models.\n- AI-driven approaches for threat detection and prediction.\n- Hands-on experience with real-world cybersecurity datasets.\n\n---\n\n## Repository Structure\n\nThis repository is organized as follows:\n\n```plaintext\n📁 AI4CyberSecWorkshop/\n├── 📁 Slides/                   # Contains the slides for the workshop presentation\n│   └── AI_in_Cybersecurity_Slides.pdf\n│\n├── 📁 src/                      # Contains the source code and Jupyter notebooks\n│   ├── Malware_Detection.ipynb  # Notebook for the Malware Detection problem\n│   ├── Time_to_Compromise.ipynb # Notebook for the Time to Compromise (TTC) problem\n│   └── 📁 data/                 # Contains synthetic datasets used in the workshop\n│       ├── malware_detection_dataset.csv  # Dataset for malware detection\n│       └── time_to_compromise_dataset.csv # Dataset for TTC prediction\n│\n└── 📄 README.md                 # Information about the workshop and repository\n```\n\n### Slides\n- The **Slides** folder contains the workshop presentation slides: \n  - [AI in Cybersecurity Slides](/slides/AI_in_Cybersecurity_Slides.pdf)\n\n### src\n- The **src** directory contains the **Jupyter notebooks** and **datasets** used for the hands-on exercises:\n  - **Malware Detection**: The `Malware_Detection.ipynb` notebook demonstrates the use of machine learning models to detect malicious software. It uses the `malware_detection_dataset.csv` dataset.\n  - **Time to Compromise (TTC) Prediction**: The `Time_to_Compromise.ipynb` notebook focuses on predicting the time it takes for an attacker to compromise a system using regression models. It uses the `time_to_compromise_dataset.csv` dataset.\n\n---\n\n\n## Getting Started\n\nTo get started with the workshop materials, follow these steps:\n\n### 1. Clone the Repository\n\nFirst, clone the repository to your local machine:\n\n```bash\ngit clone https://github.com/achrefbenammar404/AI4CyberSecWorkshop.git\ncd AI4CyberSecWorkshop\n```\n\n### 2. Create a Virtual Environment\n\nCreate a virtual environment to isolate the project dependencies:\n\n```bash\n# For Linux/Mac\npython3 -m venv venv\n\n# For Windows\npython -m venv venv\n```\n\n### 3. Activate the Virtual Environment\n\nActivate the virtual environment:\n\n```bash\n# For Linux/Mac\nsource venv/bin/activate\n\n# For Windows\nvenv\\Scripts\\activate\n```\n\n### 4. Install Dependencies\n\nWith the virtual environment activated, install the required dependencies using the provided `requirements.txt` file:\n\n```bash\npip install -r requirements.txt\n```\n\n### 5. Explore the Notebooks\n\nTo follow the hands-on exercises, open the Jupyter notebooks in the `src/` directory.\n\n---\n## Datasets\n\nThe datasets used in this workshop are synthetic datasets created for educational purposes:\n1. **malware_detection_dataset.csv**: Contains labeled data for malware detection tasks.\n2. **time_to_compromise_dataset.csv**: Contains data for predicting the time to compromise based on system and network vulnerabilities.\n\nBoth datasets are located in the `src/data/` folder.\n\n---\n\n## Workshop Agenda\n\n1. **Introduction to AI and Cybersecurity**:\n   - Overview of how AI can enhance cybersecurity and common AI techniques.\n    \u003cdiv style=\"text-align: center;\"\u003e\n        \u003cimg src=\"resources/6.jpg\" alt=\"6thSlide\" width=\"800\"/\u003e\n    \u003c/div\u003e\n\n    \u003cdiv style=\"text-align: center;\"\u003e\n        \u003cimg src=\"resources/7.jpg\" alt=\"7thSlide\" width=\"800\"/\u003e\n    \u003c/div\u003e\n\n\n2. **Supervised Learning in Cybersecurity**:\n   - Explanation of classification (malware detection) and regression (time to compromise prediction).\n   \n3. **Malware Detection**:\n   - Using machine learning models (e.g., Random Forest, Logistic Regression) to classify malicious software.\n\n4. **Time to Compromise Prediction**:\n   - Building regression models to predict how quickly a system can be compromised based on vulnerabilities and system configurations.\n\n---\n\n## Dependencies\n\nThis workshop requires the following Python libraries, which can be installed using the `requirements.txt` file:\n\n- `seaborn==0.13.2`\n- `matplotlib==3.8.2`\n- `pandas==2.2.0`\n- `numpy==1.26.1`\n- `scikit-learn==1.4.1.post1`\n- `tensorflow==2.16.1`\n\nYou can install these dependencies by running:\n\n```bash\npip install -r requirements.txt\n```\n\n---\n\n## Contributions\n\nWe encourage contributions from participants and the broader community! If you'd like to contribute, feel free to open a pull request with improvements, additional examples, or new exercises.\n\n---\n\n## License\n\nThis project is licensed under the MIT License. For more details, see the [LICENSE](LICENSE) file.\n\n---\n\n## Acknowledgments\n\nThis workshop is a collaborative effort with **IEEE ENICarthage University**. \nSpecial thanks to the organizing team.\n\n---\n\n## Contact\n\nFor any questions or feedback regarding the workshop, please reach out to us:\n\n- **Workshop Lead**: Achref Ben Ammar - [achref.benammar@enicarthage.edu.tn](mailto:achref.benammar@ieee.org)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fachrefbenammar404%2Fai4cybersecworkshop","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fachrefbenammar404%2Fai4cybersecworkshop","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fachrefbenammar404%2Fai4cybersecworkshop/lists"}