Neural Network-Based Wed Security Firewall

Neural Network-Based Wed Security Firewall

Prof. Sonali Benke1, Khule Abhijit2, Pawase Sanket3, More Vaibhav4, Thorat Siddhant5

  1Assistant Professor, Department of Computer Engineering, Sir Visvesvaraya Institute of Technology,

Nashik, Maharashtra, India.

 2,3,4,5Department of Computer Engineering, Sir Visvesvaraya Institute of Technology, Nashik, Maharashtra, India.

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Abstract

In today's digital environment, securing web applications is paramount. As cyberattacks become more sophisticated, traditional defense mechanisms struggle to keep pace. This project proposes a novel Web Application Firewall (WAF) empowered by Neural Network (NN) technology.

Our objective is to significantly bolster web application security by proactively detecting and blocking malicious attacks. The NN-based WAF leverages deep learning to analyze incoming HTTP requests in real-time, effectively differentiating between legitimate traffic and potentially harmful attempts. Through meticulous data gathering, feature engineering, and rigorous model training, the WAF achieves robust and adaptable behavior.

This project signifies a significant leap forward in web application security. By capitalizing on the power of neural networks, it delivers real-time protection against a vast array of cyber threats. The NN-based WAF's ability to adapt to the ever-changing threat landscape makes it an invaluable tool for safeguarding web applications in the face of evolving and persistent security challenges.

Keywords: Web Application Firewall, Neural Network, Cybersecurity, Real-time Protection, The NN-based WAF.