Modeling Threat Vectors in Real Time Using AI-Enhanced Surveillance Analytics Across Cyber, Land, Air, and Maritime Domains
Abstract
The convergence of digital, physical, and autonomous systems has introduced new complexity in threat detection and response. This paper presents an AI-enhanced threat modeling framework for identifying, classifying, and prioritizing risk across cyber, land, air, and maritime domains in real time. The framework uses deep neural networks, multi-sensor data fusion, and reinforcement learning agents to support adaptive situational awareness.
Type
Publication
International Journal of Research Publication and Reviews (IJRPR)
This paper introduces an AI-driven, multi-domain surveillance framework that unifies analytics across cyber, land, air, and maritime systems.
