all InfoSec news
Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning. (arXiv:2211.10782v2 [cs.LG] UPDATED)
Nov. 28, 2022, 2:10 a.m. | Mingxuan Ju, Yujie Fan, Chuxu Zhang, Yanfang Ye
cs.CR updates on arXiv.org arxiv.org
Graph Neural Networks (GNNs) have drawn significant attentions over the years
and been broadly applied to essential applications requiring solid robustness
or vigorous security standards, such as product recommendation and user
behavior modeling. Under these scenarios, exploiting GNN's vulnerabilities and
further downgrading its performance become extremely incentive for adversaries.
Previous attackers mainly focus on structural perturbations or node injections
to the existing graphs, guided by gradients from the surrogate models. Although
they deliver promising results, several limitations still exist. For …
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
SOC 2 Manager, Audit and Certification
@ Deloitte | US and CA Multiple Locations
Security Engineer 2
@ Oracle | BENGALURU, KARNATAKA, India
Oracle EBS DevSecOps Developer
@ Accenture Federal Services | Arlington, VA
Information Security GRC Specialist - Risk Program Lead
@ Western Digital | Irvine, CA, United States
Senior Cyber Operations Planner (15.09)
@ OCT Consulting, LLC | Washington, District of Columbia, United States
AI Cybersecurity Architect
@ FactSet | India, Hyderabad, DVS, SEZ-1 – Orion B4; FL 7,8,9,11 (Hyderabad - Divyasree 3)