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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:DEEPBITS TECHNOLOGY INC.
Doing Business As Name:DEEPBITS TECHNOLOGY LLC
PD/PI:
  • Xunchao Hu
  • (951) 827-6437
  • xchu@deepbitstech.com
Award Date:07/23/2021
Estimated Total Award Amount: $ 255,742
Funds Obligated to Date: $ 255,742
  • FY 2021=$255,742
Start Date:08/01/2021
End Date:07/31/2022
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:SBIR Phase I: Enabling Robust Binary Code AI via Novel Disassembly
Federal Award ID Number:2112109
DUNS ID:060752538
Program:SBIR Phase I
Program Officer:
  • Peter Atherton
  • (703) 292-8772
  • patherto@nsf.gov

Awardee Location

Street:20871 Westbury Rd.
City:Riverside
State:CA
ZIP:92508-2974
County:Riverside
Country:US
Awardee Cong. District:41

Primary Place of Performance

Organization Name:DEEPBITS TECHNOLOGY LLC
Street:3499 Tenth Street
City:RIVERSIDE
State:CA
ZIP:92501-3617
County:Riverside
Country:US
Cong. District:41

Abstract at Time of Award

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to enable novel binary code AI applications for cybersecurity, by providing a fast and accurate method to reverse-engineer executable code. Current solutions are slow and inaccurate, and the volume of code analyzed by cybersecurity applications is huge. Cybersecurity companies either deploy tremendous computing resources to handle huge volumes of code or only extract superficial features from code for AI models, making those solutions extremely vulnerable to adversarial attacks. The proposed solution will not only save computing resources but will allow for improved solutions by extracting complex features from executable code and applying more advanced AI models. This Small Business Innovation Research (SBIR) Phase I project provides an innovative disassembly solution for binary code. The lack of a fast and accurate disassembler has become an obstacle to the applications of novel AI approaches in the cybersecurity industry. The proposed solution combines state-of-the-art binary analysis techniques and newly emerging deep learning techniques to build a fast and accurate disassembler. The proposed solution utilizes GPU technology to accelerate the disassembly process. The final disassembly is expected to be over 100 times faster than state-of-the-art disassemblers, while achieving the same or better accuracy. 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.

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