This project focuses on the detection and mitigation of Distributed Denial-of-Service (DDoS) attacks in IoT (Internet of Things) and Industrial IoT (IIoT) environments using Machine Learning and Deep Learning techniques.
The system leverages advanced traffic analysis and anomaly detection methods to classify malicious and benign network traffic in real time. Using datasets such as CICIDS2023, the project applies models including:
- Random Forest
- Convolutional Neural Networks (CNN)
- Deep Learning-based anomaly detection
- Feature engineering and traffic classification techniques
The goal is to create a scalable and intelligent cybersecurity solution capable of protecting modern IoT infrastructures from evolving DDoS threats.
- 🔍 Real-time DDoS attack detection
- 🤖 Machine Learning & Deep Learning algorithms used
- 📊 Traffic classification using CICIDS2023 dataset
- 🧠 Random Forest and CNN-based anomaly detection
- 🌐 Designed for IoT and Industrial IoT environments
- ⚡ Feature selection and preprocessing pipeline
- 📈 High scalability for large network environments
- 🛡️ IoTsecurity-focused architecture
| Category | Technologies |
|---|---|
| Programming Language | Python |
| ML Libraries | Scikit-learn, TensorFlow |
| Data Handling | Pandas, NumPy |
| Visualization | Matplotlib, Seaborn |
| Deep Learning | CNN |
| Dataset | CICIDS2023 |
| Environment | Jupyter Notebook |
| Domain | Cybersecurity, IoT, IIoT |
Network Traffic
↓
Data Collection
↓
Preprocessing & Cleaning
↓
Feature Selection
↓
ML/DL Model Training
↓
Attack Classification
↓
Detection & Mitigation