Adaptive Firewall for DDOS Mitigation in AWS Cloud: A Self-Learning Security Architecture

Distributed Denial-of-Service attacks are a problem for applications that are hosted on the cloud. These Distributed

Denial-of-Service attacks often cause the applications to stop working and result in losses. Amazon Web Services does have some

built in protections like AWS Shield and WAF. However these protections are mostly static. They only react to problems after

they happen. This research is about a kind of firewall that can teach itself and adapt to Distributed Denial-of-Service attacks, in

Amazon Web Services environments. The system we propose uses AWS WAF, Shield, CloudWatch, Athena and Lambda to make

a loop that automatically gives feedback in time. The system keeps an eye on traffic logs. It looks for things that are not normal

using limits and patterns. When it finds something it changes the firewall rules to stop that bad traffic.The system also has a part

that stores information about the things it finds. This helps the system get better over time.

The people who made the system tested it with attacks to see how well it works. The system was able to find and stop these attacks

quickly. It did not make mistakes.

The system is good because it can change and get better as new threats come out. This means people do not have to fix it all the

time. The traffic logs and firewall rules and threat intelligence all work together to make the system work well. This study offers

a scalable, serverless security model for AWS that is both cost-effective and intelligent. It contributes to cloud security practices

by enabling proactive defense mechanisms that respond and learn in real time