An intelligent and secure Intrusion Detection and Prevention System (IDPS) built using machine learning techniques on the NSL-KDD dataset. This project detects and prevents network-based cyberattacks in real time with a web interface.
- 🚀 Machine Learning-based threat detection
- 📊 Trained on NSL-KDD dataset (2025)
- 🌐 Web dashboard using Flask
- 📝 Real-time logging of detected threats
- 📁 Model persistence using joblib
- File:
IDPS_Train_Model.ipynb - Dataset:
NSL-KDD-2025 - Libraries:
scikit-learn,pandas,joblib
Trains and evaluates various models to identify anomalies in network traffic.
- Entry point:
app.py - Templates:
templates/ - Static files (CSS/JS):
static/ - Frontend: Simple UI to upload logs and see detection results.
# Clone the repository
git clone https://github.com/yourusername/advanced-idps.git
cd advanced-idps
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Linux/Mac
venv\Scripts\activate # On Windows
# Install dependencies
pip install -r requirements.txt
🚀 Running the App
# Activate environment and run Flask app
python app.py
📚 Dataset
NSL-KDD 2025 is an improved version of the classic KDD Cup 1999 dataset.
Includes labeled records for DoS, Probe, R2L, and U2R attacks.
.
🤝 Contributors
Thammisetti Sreenivasulu
Cybersecurity Intern | B.Tech in CSE - Cybersecurity
GitHub: Sreenivas-147
📬 Contact
For questions or contributions, reach out at:
📧 [email protected]