Cyber-Attack Classification Using AI & ML Techniques

The rapid growth of network infrastructure and online services has increased the vulnerability of systems to cyberattacks. To mitigate such risks, our project focuses on classifying cyberattacks using machine learning algorithms. The project is designed as a web-based application, integrating both front-end and back-end technologies to provide an efficient and user - friendly interface. The user provides information to the front-end, including IP addresses, port numbers, data sizes, and protocols, while the back-end processes this data to classify the type of attack using pre-defined rules and AI-based logic. We employ a simple rule-based classifier in the first phase, with the option of incorporating more advanced machine learning algorithms such as decision trees, random forests, and neural networks in future iterations. Our system classifies common types of cyberattacks such as DDoS, SQL injection, SSL injection, and UDP flood attacks. This project aims to be a foundation for further research and enhancement in AI-driven cyber defense systems