AI-Powered Real-Time Cyber Incident Monitoring for India’s Cyberspace

DOI

10.2991/978-94-6463-940-7_8How to use a DOI?

Keywords

Deep Q Networks; Real-Time Threat Detection; MongoDB; BeautifulSoup

Abstract

The rising frequency and sophistication of cyber threats in India’s cyberspace demand a robust real-time threat intelligence system for early detection, analysis, and mitigation. Traditional cyber security measures fall short due to fragmented data sources and limited adaptive threat detection. This paper presents Threat Vision, a machine learning-driven framework that automates cyber incident detection, aggregates intelligence from multiple sources, and visualizes real-time threat trends for proactive defense. Using deep reinforcement learning (DQN) to identify and classify cyber threat-sharing platforms, alongside web scraping techniques to extract actionable intelligence, the system structures data in a centralized MongoDB database for interactive visualization. The dashboard enables cyber security professionals to monitor emerging threats by sector, Advanced Persistent Threats (APTs), and geolocation. Threat Vision demonstrates high accuracy in detecting threat-sharing platforms while overcoming challenges like dynamic web content and obfuscation techniques. By integrating adaptive learning, scalable data processing, and intuitive visualization, the system enhances situational awareness and supports rapid decision-making for security stakeholders, contributing to the advancement of real-time cyber security intelligence for India’s digital infrastructure.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Jai Sai Sashank
AU  - Varma Mamatha Deenakonda
AU  - K. I. Vishnu Vandana
AU  - M. Lavanya
AU  - J. S. S. L. Bharani
PY  - 2025
DA  - 2025/12/31
TI  - AI-Powered Real-Time Cyber Incident Monitoring for India’s Cyberspace
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 74
EP  - 83
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_8
DO  - 10.2991/978-94-6463-940-7_8
ID  - Sashank2025
ER  -