This project presents an intelligent system for detecting and classifying Distributed Denial of Service (DDoS) attacks by analyzing network traffic using Machine Learning techniques.
DDoS attacks are one of the most common and dangerous cyber threats. In these attacks, a server or network is flooded with a massive number of requests, making services unavailable for legitimate users. This can lead to service downtime, financial losses, and serious security risks.
The goal of this project is to build a smart system that can monitor network traffic behavior and automatically distinguish between normal activity and malicious traffic. Instead of relying on traditional rule-based security methods, the system learns patterns from real-world data and uses them to make accurate decisions.
The project is based on the CIC-DDoS 2019 dataset, which contains realistic network traffic data representing both normal traffic and different types of DDoS attacks. This makes the system more reliable and closer to real-world cybersecurity scenarios.
After preparing the data, a Machine Learning model is trained to recognize traffic patterns and classify them into normal or attack categories. To make the system more practical, the trained model is integrated into a web application using Flask, allowing users to interact with the system through a simple and user-friendly interface.
Through this interface, users can upload data, perform predictions, and view classification results easily. This transforms the project from a standalone model into a complete, usable system.
This project was developed as a Level 3 academic project and represents an important step in applying theoretical knowledge to real-world cybersecurity problems. It provided hands-on experience in data processing, machine learning, system integration, and collaborative work.
Overall, the system aims to provide an effective, scalable, and easy-to-use solution for early detection of DDoS attacks and improving network security and response time.
🔗 Dataset Link:
https://www.unb.ca/cic/datasets/ddos-2019.html
This project is developed for academic and educational purposes only.