MaliGNNoma addresses the security challenges faced by cloud FPGAs due to malicious circuit configurations. By utilizing graph neural networks (GNNs), it provides an effective initial security layer for multi-tenancy scenarios, outperforming existing methods. The tool demonstrates high accuracy in detecting various types of attacks, including those based on benign modules like cryptography accelerators.
The content discusses the threats posed by fault injection and side-channel attacks on cloud FPGAs, emphasizing the importance of proactive detection mechanisms. MaliGNNoma's innovative approach leverages GNNs to learn distinctive features from FPGA netlists, achieving superior performance compared to traditional scanning methods. Extensive experimentation validates MaliGNNoma's effectiveness in identifying malicious configurations with exceptional precision and accuracy.
The study also highlights the significance of transparency in explaining the classification decisions made by MaliGNNoma through sub-circuit pinpointing. The framework offers insights into specific nodes contributing to malicious classifications, enhancing understanding and analysis of netlist structures. Overall, MaliGNNoma presents a comprehensive solution for securing cloud FPGAs against evolving threats through advanced machine learning techniques.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Lilas Alrahi... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.01860.pdfDeeper Inquiries