As the number of devices connected to the internet continues to grow, so does the potential for malicious actors to launch large-scale attacks. Among them, Distributed Denial of Service (DDoS) attacks have become increasingly common in recent years. By leveraging machine learning and deep learning algorithms, network administrators can quickly identify and respond to potential threats before they become a problem. So, We have made this project as a part of our thesis to analyze network traffic and detect suspicious patterns that may indicate a DDoS attack through log file analysis. We are primarily docusing on identifying 10 different variations of DDoS attack.

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We devided our work into three modules.

• Machine Learning Approach
• Deep Learning Approach
• Visualization Tool

Yes, We have good news!! We want to make this project as open source so that you can build something more robust with our codes and if you wish to contribute here, just "Pull Request" → modify something usefull (not just random stuff !!) → Then, Commit and Push your modified code to a new branch → After analysis our Expert Team will merge that piece of code to master branch and as a reward you will be provided with contributor logo. Isn't it exciting!!

If you have any suggestions, queries, or comments, kindly post them in the discussion section up top. And, If you would want to work with us or finish your project, then yes, we are actively seeking for high-paying jobs.