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One aspect of system security is evaluating a system’s vulnerability to Trojan attack. A hardware Trojan attack can have potentially devastating effects, especially given the increased reliance on integrated circuits within critical systems. A significant amount of research concerns attacks on digital systems, but attacks on AMS and RF systems have recently been of interest as well. A class of Trojans has been proposed that uses undesired alternate modes of operation in nonlinear systems as the Trojan payload. These Trojans are of particular interest because they do not cause deviations from the ideal system performance and cannot be detected until the Trojan is triggered. This work addresses this class of Trojans by listing different payloads, trigger mechanisms, and examples of system architectures vulnerable to attack.
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Design of AI Trojans for Evading Machine Learning-based Detection of Hardware Trojans
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-of-the art approaches based on adversarial attacks.
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- Award ID(s):
- 1908131
- PAR ID:
- 10354090
- Date Published:
- Journal Name:
- Design, Automation & Test in Europe Conference & Exhibition (DATE)
- Page Range / eLocation ID:
- 682 to 687
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/DATE54114.2022.9774654
