Hardware Trojan

Hardware Trojans (HTs) are malicious modifications secretly embedded in integrated circuits (ICs), posing a significant threat to security and functionality. Current research focuses on developing both detection and insertion methods using machine learning, particularly graph neural networks (GNNs) and reinforcement learning (RL), to create more realistic and effective adversarial examples. These advancements aim to improve the accuracy and efficiency of HT detection, while simultaneously revealing vulnerabilities in existing defense mechanisms, ultimately contributing to more robust and secure IC design and verification processes.

Papers

March 4, 2022

Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores
Nikhil Muralidhar, Abdullah Zubair, Nathanael Weidler, Ryan Gerdes, Naren Ramakrishnan
Embedded System Contrastive Graph Hardware Trojan Integrated Circuit Design Hardware Trojan Detection Malicious Content Generation

November 29, 2021

Third-Party Hardware IP Assurance against Trojans through Supervised Learning and Post-processing
Pravin Gaikwad, Jonathan Cruz, Prabuddha Chakraborty, Swarup Bhunia, Tamzidul Hoque
Supervised Learning Post Processing Trojan Attack Trojan Detection Hardware Trojan

Hardware Trojan

Papers

Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores

Third-Party Hardware IP Assurance against Trojans through Supervised Learning and Post-processing