SBIR Phase I: Enabling Robust Binary Code AI via Novel Disassembly

SBIR 第一阶段：通过新颖的反汇编实现鲁棒的二进制代码 AI

• 批准号: 2112109
• 负责人: Xunchao Hu
• 金额: $ 25.57万
• 依托单位: DEEPBITS TECHNOLOGY LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112109&HistoricalAwards=false
• 关键词: SBIR; Phase; Enabling; Robust; Binary

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.

小型企业创新研究(SBIR)第一阶段项目的更广泛影响是，通过提供快速而准确的方法对可执行代码进行反向工程，使新型二进制代码AI应用能够用于网络安全。目前的解决方案速度慢且不准确，网络安全应用程序分析的代码量巨大。网络安全公司要么部署大量计算资源来处理大量代码，要么只从人工智能模型的代码中提取表面特征，这使得这些解决方案极易受到对手的攻击。建议的解决方案不仅将节省计算资源，还将通过从可执行代码中提取复杂特征并应用更先进的人工智能模型来改进解决方案。这个小型企业创新研究(SBIR)第一阶段项目提供了一种创新的二进制代码反汇编解决方案。缺乏快速准确的反汇编程序已成为新型人工智能方法在网络安全行业应用的障碍。提出的解决方案结合了最先进的二进制分析技术和新出现的深度学习技术，以构建快速而准确的反汇编程序。提出的解决方案利用GPU技术来加速拆卸过程。最终的拆卸速度预计将比最先进的拆装机快100倍以上，同时实现相同或更高的精确度。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。