Adversarial Techniques for Bypassing Graph Neural Networks Based Network Defense
Kartikeya Sharma
SAINTCON 2025 · Day 3 · Main Track 1
In the realm of modern cybersecurity, **Graph Neural Networks (GNNs)** have emerged as a promising technology for detecting sophisticated network intrusions, particularly Distributed Denial of Service (DDoS) attacks. These advanced **deep learning models**, capable of analyzing complex relational patterns within network traffic, have shown remarkable accuracy, with some state-of-the-art implementations claiming up to 99% detection rates on open-source datasets. However, this talk by Kartikeya Sharma, a Senior Associate Information Security Engineer at Equinix, in collaboration with Dr. Jun Lee from the University of Oregon, unveils a critical vulnerability in these seemingly robust defenses.