HMGAT-ID: A Heterogeneous Multimodal Graph Attention Network for Intelligent IoT Intrusion Detection

Project Abstract

Securing distributed IoT nodes using multimodal graph attention networks to detect micro-anomalies across heterogeneous communication links.

Designed to address domain scalability challenges, this engineering prototype validates predictive modeling and computational analysis. It provides clean deployment scripts, structured models, and evaluation routines suitable for academic and industrial research labs.

Core System Features

• Optimized Modeling Pipeline: Uses state-of-the-art architectures to achieve stable convergence and high-accuracy scoring.
• Defect-Aware & Domain Adaptation Controls: Incorporates error correction routines and adaptive filtering layers to boost cross-system generalizability.
• Practical Implementation: Developed as a model for university lab cell testing and local prototyping trials.