Benchmarking Graph-Based Models for Real-Time IoT Anomaly Detection
Document Type
Conference Proceeding
Publication Date
4-2025
Abstract
The exponential growth of IoT networks introduces new security vulnerabilities that are often exploited in real time. This research poster hypothesizes that while current state-of-the-art (SOTA) real-time detection algorithms offer promising performance, they are still hindered by issues related to detection reliability, resource constraints, and adaptability in dynamic network environments. This study focuses on benchmarking graph-based models— for real-time anomaly detection using a standardized IoT attack dataset. These models leverage the structural and temporal properties of network traffic graphs to detect anomalies. We aim to evaluate their accuracy, detection latency, and computational efficiency under IoT-specific constraints. By identifying performance gaps and contextual challenges, this research will inform the development of more adaptive and lightweight graph-based detection mechanisms. The findings will contribute to advancing security in IoT networks and guiding future innovation in real-time threat detection.
Recommended Citation
Marquez, Eirik, "Benchmarking Graph-Based Models for Real-Time IoT Anomaly Detection" (2025). Student Research Symposium 2025. 65.
https://digitalcommons.tamusa.edu/srs_2025/65
Comments
Poster Session 2
5:30-7:00 p.m.
BLH Lobby