Project Description
Aegis is an Artificial Neural Network (ANN)-based Intrusion Detection System designed to safeguard networks by proactively identifying malicious activity and differentiating it from normal traffic. As a Free and Open Source Software (FOSS) project, Aegis embraces the principles of transparency, collaboration, and community-driven innovation.
Key Features
- Enhanced Detection: Employs a meticulously trained ANN model for superior detection accuracy of network attacks.
- Data Preprocessing: Thorough data cleaning and normalization to optimize model performance.
- Dimensionality Reduction: Leverages techniques like Principal Component Analysis (PCA) for efficient feature representation.
- Customizable Architecture: Flexible ANN architecture adaptable to diverse network environments.
- Performance Visualization: Provides graphical insights into model training and evaluation metrics.
Architecture
- Data Input (Raw network data)
- Preprocessing
- Data cleaning & normalization
- Feature encoding
- Dimensionality Reduction (Principal Component Analysis)
- ANN Model
- Input Layer
- Hidden Layers (Dense with ReLU activation)
- Output Layer (Sigmoid activation for binary classification)
- Detection Output (Normal traffic vs. Attack)
Getting Started
- Prerequisites:
- Python 3.x
- Libraries: pandas, numpy, matplotlib, scikit-learn, tensorflow
-
Clone Repository:
git clone https://github.com/thisisarnabdas/aegis.git -
Run:
python main.ipynb -
Run on Google Colab: Click the "Open In Colab" badge above to run Aegis directly in your browser using Google Colab.
Dataset
- Utilize the KDD Cup 1999 dataset
Technologies
- Python
- Pandas
- NumPy
- Scikit-learn
- TensorFlow
- Keras
- Matplotlib
Future Development
- Real-time Detection: Explore streaming data integration.
- Hyperparameter Tuning: Automated hyperparameter optimization.
- Ensemble Techniques: Experiment with combining multiple models.
Contributing
We welcome contributions to improve and expand Aegis! Feel free to submit issues, feature requests, and pull requests. As a FOSS project, we believe in the power of collaboration and invite developers, researchers, and security enthusiasts to join our mission of building robust network defenses.
Contact
ARNAB DAS - arnab.das@g.bracu.ac.bd
AVIZIT SARKAR - avizit.sarkar@g.bracu.ac.bd
✨ Embrace the Freedom of Open Source and Secure Your Digital Realm! ✨