10EFX - Cybersecurity Attack Detection and Optimization

10EFX is a machine learning-based project designed to identify, classify, and optimize cybersecurity threats using advanced techniques. The main goal of this project is to detect various types of cyberattacks and enhance model performance through hyperparameter optimization and ensemble learning. Clone the Repository

To clone this repository, use the following command:

gh repo clone s3loc/A10EFX

Current Status

At its current stage, the project primarily focuses on:

Attack Detection and Classification: Utilizing machine learning classifiers (XGBoost, LightGBM, CatBoost) to detect and classify different types of cyberattacks within a given dataset.
Data Preprocessing and Feature Engineering: Techniques such as SMOTE for data augmentation, feature scaling (RobustScaler), and feature selection (SelectKBest and mutual_info_classif) are applied.
Hyperparameter Optimization: Using tools like Optuna and RandomSearch to fine-tune hyperparameters, improving model performance.
Ensemble Learning: Combining multiple classifiers into an ensemble model to enhance prediction accuracy and robustness.
Model Interpretability: SHAP (SHapley Additive exPlanations) is utilized to explain and interpret the decision-making process of the models.

Project Overview Technologies and Libraries:

Machine Learning: XGBoost, LightGBM, CatBoost, Scikit-learn
Deep Learning: TensorFlow, Keras (for model training and GAN-based data augmentation)
Data Processing: Pandas, NumPy, Scikit-learn (for preprocessing, feature selection, scaling)
Optimization: Optuna, RandomSearch
Ensemble Learning: Voting Classifier (ensemble of classifiers)
Model Explainability: SHAP
Data Augmentation: SMOTE, GAN-based methods
Version Control and Deployment: Git, GitHub

Steps Taken in the Project:

Data Collection and Preprocessing: The dataset was cleaned, missing values imputed, and various preprocessing and feature engineering techniques applied.
Modeling: Several machine learning models were trained and optimized using hyperparameter tuning for attack detection.
Ensemble Learning: The best-performing models were integrated into an ensemble to improve prediction accuracy.
Model Evaluation: Key performance metrics such as accuracy, F1-score, ROC-AUC, precision, and recall were evaluated.
Model Interpretability: SHAP was employed to provide transparency and interpret the predictions of the model.

Future Directions

While the project is progressing well in the domain of attack detection and classification, the ultimate goal is to transform 10EFX into a comprehensive tool for Red Team operations and penetration testing. Some future directions include:

Attack Simulation: Implementing reinforcement learning or adversarial training to simulate a variety of attack vectors and develop new tactics.
Exploit Development: Integrating AI with automated exploit development to test and simulate real-world cyberattacks.
Real-Time Attack Detection: Enhancing the model's ability to perform real-time detection using time-series data (e.g., network traffic).
Red Team Tool Integration: Developing an end-to-end Red Team AI tool for autonomously testing and exploiting vulnerabilities in a system, mimicking real-world attack strategies.
Ethical and Legal Considerations: Ensuring ethical and legal compliance in using AI for cybersecurity purposes.

Getting Started

To get started with the project:

Clone the Repository:

gh repo clone s3loc/A10EFX cd A10EFX

Install Dependencies:

pip install -r requirements.txt

Run the Project: Follow the specific instructions for running different parts of the project (model training, attack detection, etc.) in the respective scripts.

Contributions

Contributions are welcome! Feel free to fork this repository, make changes, and submit pull requests. For new feature suggestions or issues, please create an issue or contact us directly. License

This project is licensed under the MIT License - see the LICENSE file for more details.

YOLO