45 citations · 265 across the 19 of their papers we have counts for
24 papers · 1 filter
Securing Cloud FPGAs Against Power Side-Channel Attacks: A Case Study on Iterative AES
Nithyashankari Gummidipoondi Jayasankaran, Hao Guo, Satwik Patnaik +3
The various benefits of multi-tenanting, such as higher device utilization and increased profit margin, intrigue the cloud field-programmable gate array (FPGA) servers to include m…
PoisonedGNN: Backdoor Attack on Graph Neural Networks-based Hardware Security Systems
Lilas Alrahis, Satwik Patnaik, Muhammad Abdullah Hanif +2
Graph neural networks (GNNs) have shown great success in detecting intellectual property (IP) piracy and hardware Trojans (HTs). However, the machine learning community has demonst…
Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges
Satwik Patnaik, Vasudev Gohil, Hao Guo +2
Reinforcement learning (RL) is a machine learning paradigm where an autonomous agent learns to make an optimal sequence of decisions by interacting with the underlying environment.…
ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning
Vasudev Gohil, Hao Guo, Satwik Patnaik +2
Stealthy hardware Trojans (HTs) inserted during the fabrication of integrated circuits can bypass the security of critical infrastructures. Although researchers have proposed many…
Embracing Graph Neural Networks for Hardware Security (Invited Paper)
Lilas Alrahis, Satwik Patnaik, Muhammad Shafique +1
Graph neural networks (GNNs) have attracted increasing attention due to their superior performance in deep learning on graph-structured data. GNNs have succeeded across various dom…
MuxLink: Circumventing Learning-Resilient MUX-Locking Using Graph Neural Network-based Link Prediction
Lilas Alrahis, Satwik Patnaik, Muhammad Shafique +1
Logic locking has received considerable interest as a prominent technique for protecting the design intellectual property from untrusted entities, especially the foundry. Recently,…