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Deep Web Guard – AI Powered Security Platform

Author

Listed:
  • Dr. Ramesh Koppar

    (Associate Professor CSE Department, Sai Vidya Institute of Technology, Bangalore)

  • Prof Poornima Gowda H. S.

    (Associate Professor CSE Department, Sai Vidya Institute of Technology, Bangalore)

  • P. Vaishnavi

    (Student CSE Department, Sai Vidya Institute of Technology, Bangalore)

  • Spandana S. M.

    (Student CSE Department, Sai Vidya Institute of Technology, Bangalore)

  • Varshitha N.

    (Student CSE Department, Sai Vidya Institute of Technology, Bangalore)

  • Nikita

    (Student CSE Department, Sai Vidya Institute of Technology, Bangalore)

Abstract

The Deep Web Guard project presents an AI-powered, dual-mode cybersecurity platform that integrates a Web Vulnerability Scanner and a Network Intrusion Detection System (NIDS) into a unified solution. Designed to provide end-to-end protection for web applications and network infrastructures, the platform leverages GPTbased threat intelligence and machine learning algorithms for real-time anomaly detection, risk scoring, and contextual threat analysis. The system combines static and dynamic web scanning, payload fuzzing, and signature-based as well as ML-driven network monitoring to detect both known and zero-day attacks. Developed using Python, Node.js, and React, the platform offers an intuitive dashboard for real-time visualization, automated reporting, and alert management. By reducing false positives and simplifying deployment, Deep Web Guard delivers an affordable, open-source, and intelligent cybersecurity framework suitable for organizations of all sizes.

Suggested Citation

  • Dr. Ramesh Koppar & Prof Poornima Gowda H. S. & P. Vaishnavi & Spandana S. M. & Varshitha N. & Nikita, 2026. "Deep Web Guard – AI Powered Security Platform," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 10(19), pages 666-681, February.
    • Handle: RePEc:bcp:journl:v:10:y:2026:i:19:p:666-681
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