Jiameng Shi (University of Georgia), Wenqiang Li (Independent Researcher), Wenwen Wang (University of Georgia), Le Guan (University of Georgia)

Although numerous dynamic testing techniques have been developed, they can hardly be directly applied to firmware of deeply embedded (e.g., microcontroller-based) devices due to the tremendously different runtime environment and restricted resources on these devices. This work tackles these challenges by leveraging the unique position of microcontroller devices during firmware development. That is, firmware developers have to rely on a powerful engineering workstation that connects to the target device to program and debug code. Therefore, we develop a decoupled firmware testing framework named IPEA, which shifts the overhead of resource-intensive analysis tasks from the microcontroller to the workstation. Only lightweight “needle probes” are left in the firmware to collect internal execution information without processing it. We also instantiated this framework with a sanitizer based on pointer capability (IPEA-San) and a greybox fuzzer (IPEA-Fuzz). By comparing IPEA-San with a port of AddressSanitizer for microcontrollers, we show that IPEA-San reduces memory overhead by 62.75% in real-world firmware with better detection accuracy. Combining IPEA-Fuzz with IPEA-San, we found 7 zero-day bugs in popular IoT libraries (3) and peripheral driver code (4).

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Private Aggregate Queries to Untrusted Databases

Syed Mahbub Hafiz (University of California, Davis), Chitrabhanu Gupta (University of California, Davis), Warren Wnuck (University of California, Davis), Brijesh Vora (University of California, Davis), Chen-Nee Chuah (University of California, Davis)

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Low-Quality Training Data Only? A Robust Framework for Detecting...

Yuqi Qing (Tsinghua University), Qilei Yin (Zhongguancun Laboratory), Xinhao Deng (Tsinghua University), Yihao Chen (Tsinghua University), Zhuotao Liu (Tsinghua University), Kun Sun (George Mason University), Ke Xu (Tsinghua University), Jia Zhang (Tsinghua University), Qi Li (Tsinghua University)

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LDR: Secure and Efficient Linux Driver Runtime for Embedded...

Huaiyu Yan (Southeast University), Zhen Ling (Southeast University), Haobo Li (Southeast University), Lan Luo (Anhui University of Technology), Xinhui Shao (Southeast University), Kai Dong (Southeast University), Ping Jiang (Southeast University), Ming Yang (Southeast University), Junzhou Luo (Southeast University, Nanjing, P.R. China), Xinwen Fu (University of Massachusetts Lowell)

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Make your IoT environments robust against adversarial machine learning...

Hamed Haddadpajouh (University of Guelph), Ali Dehghantanha (University of Guelph)

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