Browsing by Author "Li, Zhi"

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    Simultaneous measurements of multiple flow parameters for scramjet characterization using tunable diode-laser sensors
    Li, Fei; Yu, XiLong; Gu, Hongbin; Li, Zhi; Zhao, Yan; Ma, Lin; Chen, Lihong; Chang, Xinyu (Optical Society of America, 2011-12-01)
    This paper reports the simultaneous measurements of multiple flow parameters in a scramjet facility operating at a nominal Mach number of 2.5 using a sensing system based on tunable diode-laser absorption spectroscopy (TDLAS). The TDLAS system measures velocity, temperature, and water vapor partial pressure at three different locations of the scramjet: the inlet, the combustion region near the flame stabilization cavity, and the exit of the combustor. These measurements enable the determination of the variation of the Mach number and the combustion mode in the scramjet engine, which are critical for evaluating the combustion efficiency and optimizing engine performance. The results obtained in this work clearly demonstrated the applicability of TDLAS sensors in harsh and high-speed environments. The TDLAS system, due to its unique virtues, is expected to play an important role in the development of scramjet engines. (C) 2011 Optical Society of America
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    Understanding Security Risks of Embedded Devices Through Fine-Grained Firmware Fingerprinting
    Li, Qiang; Tan, Dawei; Ge, Xin; Wang, Haining; Li, Zhi; Liu, Jiqiang (IEEE, 2022-11)
    An increasing number of embedded devices are connecting to the Internet, ranging from cameras, routers to printers, while an adversary can exploit security flaws already known to compromise those devices. Security patches are usually associated with the device firmware, which relies on the device vendors and products. Due to compatibility and release-time issues, many embedded devices are still using outdated firmware with known vulnerabilities or flaws. In this article, we conduct a systematic study on device vulnerabilities by leveraging firmware fingerprints. Specifically, we use a web crawler to gather 9,716 firmware images from official websites of device vendors, and 347,685 security reports scattered across data archives, blogs, and forums. We propose to generate fine-grained fingerprints based on the subtle differences between the filesystems of various firmware images. Furthermore, machine learning algorithms and regex are used to identify device vulnerabilities and corresponding device firmware fingerprints. We perform real-world experiments to validate the performance of the firmware fingerprint, which yields high accuracy of 91% precision and 90% recall. We reveal that 6,898 reports have the firmware and related vulnerability information, and there are more than 10% of firmware vulnerabilities without any patches or solutions for mitigating underlying security risks.