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Active CONTINUING GRANT National Science Foundation (US)

CAREER: Security-Aware Next Generation Embedded Hardware Design

$3.48M USD

Funder National Science Foundation (US)
Recipient Organization University of Texas At Dallas
Country United States
Start Date Mar 01, 2025
End Date Feb 28, 2030
Duration 1,825 days
Number of Grantees 1
Roles Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2440807
Grant Description

The widespread proliferation of computing devices embedded into everyday products has ushered in an era of ubiquitous production and dissemination of malware, computer software that has the intent to cause damage. Traditional antivirus systems to protect against malware are ineffective due to their low accuracy in identifying modern, sophisticated malware.

Additionally, antivirus software incurs high overhead on resource-constrained embedded platforms. This has propelled the development of hardware-assisted malware detectors, which use the trusted underlying hardware to help detect malware. However, detection based on hardware performance counters faces several inherent pitfalls, such as high false positives in malware detection.

This research proposes an end-to-end framework for developing, analyzing, and securing fine-grained design-for-security primitives, which can be incorporated into the embedded hardware. This research enhances the effectiveness of hardware-assisted security solutions, leading to lightweight and robust design-for-security primitives for resource-constrained embedded devices utilized in applications such as automotive, medical, and military.

The educational plan will enhance courses at both undergraduate and graduate levels by introducing hands-on experiences in hardware security. An educational game will be designed to improve K-12 students’ understanding of malware.

The project develops security-aware design principles for next-generation embedded hardware, which includes meticulously crafted design-for-security primitives comprising instruction sequences and debug-level register information. The new algorithmic approaches for trace analysis utilize time-series and explainability-based classification to improve malware detection performance.

This research also investigates methods to secures the fine-grained design-for-security primitives against adversarial and snooping attacks by developing novel defense strategies based on theoretical foundations. Research findings are being integrated into undergraduate and graduate course materials, embedded hardware design camps for high-school students, and an educational prototype game to ignite the passions of future investigative minds in embedded hardware security.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

All Grantees

University of Texas At Dallas

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