This project focuses on developing a cloud-based DDoS (Distributed Denial of Service) prevention system using Machine Learning (ML) and Deep Learning (DL) models. The solution aims to detect and mitigate DDoS attacks in real-time, with the ability to learn and adapt to new attack patterns as they emerge.
- ⚡ Real-time DDoS Detection: Monitors cloud traffic and identifies malicious activities.
- 🧠 Adaptive Learning: Uses Incremental Learning Models (ILMs) to continuously learn and improve its defense mechanisms against new attack patterns.
- 🔄 Hybrid ML/DL Architecture: Combines traditional ML for known attack types with ILM for novel, zero-day attacks.
- 🕵️♂️ Honeypot Integration: Optionally incorporates honeypots for deep analysis of suspicious traffic.
- 🔍 Anomaly Detection: Detects abnormal traffic patterns using anomaly detection techniques.
- Data Collection & Preprocessing: Traffic logs are continuously collected and processed for feature extraction.
- Modeling: A hybrid of traditional supervised learning and Incremental Learning is used to identify known and new DDoS attacks.
- Real-Time Decision Making: The system employs online learning models to adapt to new traffic patterns in real-time.
- Feedback Loop: Detected attacks are labeled and used to retrain the model, ensuring the system keeps evolving.
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Clone the Repository
git clone https://github.com/deepesh611/Minor-Project-DDoS-on-Cloud.git cd Minor-Project-DDoS-on-Cloud -
Run Setup: Ensure you have Python 3.11 or 3.12 installed, then open powershell and run:
.\setup.sh
- ☁️ Cloud Auto-scaling: Implement an automated cloud scaling mechanism based on detected traffic loads.
- 🕸️ Advanced Honeypots: Use more sophisticated honeypots for detailed analysis of attackers' behavior.
Thanks goes to these wonderful people:
 Deepesh Patil 💻 📖 🔬 🤔 🖋 |
 Sakshamdharmik 💻 🔬 🖋 🤔 |
 Naman Goyal 🖋 🤔 📖 🔬 |
 imcoder44 📖 💻 🖋 |