Design of AI Trojans for Evading Machine Learning-based Detection of Hardware Trojans
Zhixin Pan and Prabhat Mishra
Department of Computer & Information Science & Engineering University of Florida, Gainesville, Florida, USA
ABSTRACT
The globalized semiconductor supply chain significantly increases the risk of exposing System-on-Chip (SoC) designs to malicious implants, popularly known as hardware Trojans. Traditional simulation-based validation is unsuitable for detection of carefully-crafted hardware Trojans with extremely rare trigger conditions. While machine learning (ML) based Trojan detection approaches are promising due to their scalability as well as detection accuracy, ML-based methods themselves are vulnerable from Trojan attacks. In this paper, we propose a robust backdoor attack on ML-based Trojan detection algorithms to demonstrate this serious vulnerability. The proposed framework is able to design an AI Trojan and implant it inside the ML model that can be triggered by specific inputs. Experimental results demonstrate that the proposed AI Trojans can bypass state-of-the-art defense algorithms. Moreover, our approach provides a fast and cost-effective solution in achieving 100% attack success rate that significantly outperforms state-ofthe art approaches based on adversarial attacks.
