Deep Web Guard – AI Powered Security Platform

Authors

Dr. Ramesh Koppar

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

Prof Poornima Gowda H. S.

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

P. Vaishnavi

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

Spandana S. M.

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

Varshitha N.

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

Nikita

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

Article Information

DOI: 10.47772/IJRISS.2026.10190060

Subject Category: Cybersecurity

Volume/Issue: 10/19 | Page No: 666-681

Publication Timeline

Submitted: 2026-01-28

Accepted: 2026-02-02

Published: 2026-02-18

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.

Keywords

AI-powered security platform, web vulnerability scanner

Downloads

References

1. B. Schölkopf et al., “Estimating the Support of a High-Dimensional Distribution,” Neural Computation, vol. 13, no. 7, pp. 1443–1471, 2001. [Google Scholar] [Crossref]

2. C. Yin et al., “Deep Learning-Based Intrusion Detection Systems: A Survey,” IEEE Access, vol. 9, pp. 144372–144396, 2021. [Google Scholar] [Crossref]

3. D. Subba et al., “Multi-Layer Correlation-Based Intrusion Detection: A Survey,” Journal of Information Security and Applications, vol. 75, 2023. [Google Scholar] [Crossref]

4. F. T. Liu, K. M. Ting, and Z.-H. Zhou, “Isolation Forest,” in Proc. IEEE ICDM, 2008, pp. 413–422. [Google Scholar] [Crossref]

5. H. Kim et al., “AI-Augmented Post-Classification Pipelines for Security Operations,” IEEE Access, vol. 12, pp. 99989–100012, 2024. [Google Scholar] [Crossref]

6. Hameed et al., “A Survey of Hybrid Intrusion Detection Systems,” Journal of Network Security, vol. 42, no. 3, pp. 112–130, 2022. [Google Scholar] [Crossref]

7. M. Al-Lail and V. García, “Machine Learning for Network Intrusion Detection—A Survey,” Future Internet, vol. 15, no. 7, 2023. [Google Scholar] [Crossref]

8. M. Cao et al., “Generative AI for Security Analytics,” IEEE Security & Privacy, vol. 21, no. 5, pp. 57–67, 2023. [Google Scholar] [Crossref]

9. M. Roesch, “Snort—Lightweight Intrusion Detection for Networks,” USENIX LISA, 1999.Available: https://www.usenix.org/legacy/event/lisa99/full_papers/roesch/roesch.pdf [Google Scholar] [Crossref]

10. M. Zolanvari et al., “Ensemble and Correlation-Based Hybrid IDS Frameworks,” IEEE Communications Surveys & Tutorials, vol. 24, no. 2, 2022. [Google Scholar] [Crossref]

11. P. Devadiga, “AI-Based Web Vulnerability Scanner: A Comprehensive Review,” SSRN preprint, 2024.Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5102292 [Google Scholar] [Crossref]

12. Prakash et al., “Cross-Layer Attack Correlation in Web-to-Network Kill-Chains,” IEEE Transactions on Dependable and Secure Computing, 2023. [Google Scholar] [Crossref]

13. R. Karim et al., “Survey of Web Application Vulnerabilities and Open-Source Scanners,” Computers & Security, vol. 125, 2022. [Google Scholar] [Crossref]

14. R. Shah and J. Patel, “LLM-Assisted Cybersecurity: Opportunities and Risks,” arXiv:2401.01234, 2024. [Google Scholar] [Crossref]

15. S. Iqbal et al., “A Systematic Review of Hybrid NIDS Architectures,” ACM Computing Surveys, vol. 55, no. 8, 2022. [Google Scholar] [Crossref]

16. S. Sinha and O. Bello, “Using GPT-Like Models for Vulnerability Explanation and Triaging,” Applied Sciences, vol. 14, no. 2, 2024. [Google Scholar] [Crossref]

17. T. Nguyen et al., “False Positive Reduction in ML-Driven IDS: A Survey,” Expert Systems with Applications, vol. 230, 2023. [Google Scholar] [Crossref]

18. U. Fiore et al., “Hybrid Intrusion Detection Systems: Design and Evaluation,” Computers & Security, vol. 106, 2023. [Google Scholar] [Crossref]

19. V. Paxson, “Bro: A System for Detecting Network Intruders in Real-Time,” in Proc. 7th USENIX Security Symposium,1999. [Google Scholar] [Crossref]

20. Available:https://www.usenix.org/publications/library/proceedings/sec98/full_papers/paxson/paxson.pdf [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles