HOW THE POWER OF MACHINE LEARNING, DATA SCIENCE, AND ADVANCED NLP CAN BE USED TO PREVENT SPOOFING AND REDUCE FINANCIAL RISKS: AN INTELLIGENT ZERO-TRUST ARCHITECTURE (VERSION 2)

Spoofing attacks and socially engineered financial fraud continue to escalate in scale and sophistication, exceeding the detection capabilities of traditional rule-based security mechanisms.This paper presents an advanced second-generation framework titled "How the Power of Machine Learning, Data Science, and Advanced NLP Can Be Used to Prevent Spoofing and Reduce Financial Risks: An Intelligent Zero-Trust Architecture (Version 2)", which introduces a scalable, intelligent cybersecurity architecture designed for high-risk financial and enterprise environments.The proposed architecture integrates machine learning, advanced natural language processing, and behavioral intelligence within a continuous zero-trust validation model.It introduces a multi-layered spoofing detection engine that combines transformer-based NLP models for semantic intent analysis, behavioral fingerprinting for identity consistency verification, and adaptive trust scoring for real-time decision making.A graph-based trust model is proposed to dynamically represent relationships Sasibhushan Rao Chanthati https://iaeme.com/