This Paper Presents An AI-Driven Secure Network Authentication System With Spatial Trust Validation To Enhance Traditional Login Security Using Machine Learning And Generative AI Techniques. During Registration, The System Stores Baseline Authentication Features Including IP Address And Geolocation Along With User Credentials. During Login, Real-time IP And Location Data Are Compared With Stored Values To Detect Anomalies.An Intrusion Detection System Based On The XGBoost Algorithm Analyzes Behavioral Features Such As Login Frequency, IP Deviation, Geolocation Variance, And Failed Attempts To Classify Access Requests And Generate A Dynamic Risk Score. Suspicious Activities Trigger Instant Email Alerts And Are Logged For Analysis. Additionally, A Generative AI Module Provides Adaptive Security Recommendations. The System Establishes A Smart And Self-learning Cyber Security Framework Suitable For Modern Web Applications.The Integration Of Spatial Validation With Behavioral Analysis Significantly Reduces Unauthorized Access Risks. The Proposed Framework Supports Real-time Monitoring And Dynamic Decision-making To Strengthen Overall Network Security. Experimental Evaluation Demonstrates Improved Detection Accuracy And Reduced False Positives Compared To Conventional Authentication Systems.
K et al. (Wed,) studied this question.