Modeling Threat Vectors in Real-Time Using AI-Enhanced Surveillance Analytics . . .
Abstract
The growing convergence of digital, physical, and autonomous systems has introduced unprecedented complexity in modern threat detection and response.
This paper presents a unified AI-enhanced threat modeling framework capable of identifying, classifying, and prioritizing risks across cyber, land, air, and maritime domains in real time.
The system leverages deep neural networks, multi-sensor data fusion, and reinforcement learning agents to create a continuous, adaptive situational-awareness model.
Empirical evaluations show a 47 % improvement in cross-domain detection accuracy and a 32 % reduction in false alarms compared to traditional intelligence analytics.
The framework demonstrates the potential of multi-domain AI analytics to enhance national defense, border protection, and critical infrastructure resilience.
This paper presents a unified AI-enhanced threat modeling framework capable of identifying, classifying, and prioritizing risks across cyber, land, air, and maritime domains in real time.
The system leverages deep neural networks, multi-sensor data fusion, and reinforcement learning agents to create a continuous, adaptive situational-awareness model.
Empirical evaluations show a 47 % improvement in cross-domain detection accuracy and a 32 % reduction in false alarms compared to traditional intelligence analytics.
The framework demonstrates the potential of multi-domain AI analytics to enhance national defense, border protection, and critical infrastructure resilience.
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.
By fusing multi-sensor data and deep-learning insights, the system models threat behaviors in real time to strengthen national defense and infrastructure protection.
