Detecting Firmware Vulnerabilities in Smart Home Devices

检测智能家居设备中的固件漏洞

• 批准号: LP180100170
• 负责人: Prof Yang Xiang
• 金额: $ 25.3万
• 依托单位: Swinburne University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2019
• 资助国家: 澳大利亚
• 起止时间: 2019-07-29 至 2024-06-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP180100170
• 关键词: Detecting; Firmware; Vulnerabilities; Smart; Home

83% of Australians have smart home devices. 47% claim they have three or more. These devices are easily targeted by cyber-attacks, and searching for their vulnerabilities has become more crucial than ever. Our industry partner GPG is actively looking for ways to detect vulnerabilities in their smart home products, but have not found any existing methods that satisfy three critical requirements: 1) massive search, 2) cross platform detection, and 3) finding unseen vulnerabilities. We therefore propose to use a series of new techniques such as efficient in-memory fuzzing, conditional formulas, and transfer learning to solve the above challenges. The project outcomes will help Australia gain cutting edge techniques in vulnerability detection.

83％的澳大利亚人拥有智能家居设备。 47％的人声称他们有三个或更多。这些设备很容易受到网络攻击的针对性，而寻找其脆弱性已经比以往任何时候都更加重要。我们的行业合作伙伴GPG积极寻找方法来检测其智能家居产品中的脆弱性，但尚未找到满足三个关键要求的现有方法：1）大规模搜索，2）跨平台检测，3）寻找看不见的漏洞。因此，我们建议使用一系列新技术，例如有效的内存模糊，条件公式和转移学习来解决上述挑战。该项目结果将有助于澳大利亚在脆弱性检测中获得最先进的技术。