An efficient mining method for DSP undocumented instruction

Xingcan Zhang; Jian Wang; Changlong Liu; Bin Li

doi:10.1117/12.2681569

1 June 2023 An efficient mining method for DSP undocumented instruction

Xingcan Zhang, Jian Wang, Changlong Liu, Bin Li

Author Affiliations +

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 1271816 (2023) https://doi.org/10.1117/12.2681569
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

Nowadays, DSP processors have been widely used in wireless network systems, since it can meet high-speed real-time information processing requirements. Meanwhile, the undocumented instructions in processors, with potentially catastrophic consequences, have become one of the main threats to system security. Therefore, the mining of undocumented instructions in processors is quite crucial for reinforcing the security of wireless network. To achieve this goal, we propose an undocumented instruction search method specifically for DSP processors, which can efficiently and accurately obtain undocumented instructions. First, to compress the instruction search space, we analyze the characteristics of DSP instruction format. Second, we build a precise instruction disassembly framework to identify all the undefined instructions. Finally, we build an automatic test tool for the executability of undefined instructions. We perform extensive experiments to evaluate the effectiveness of our method. The results reveal that our approach is valid for mining undocumented instructions in DSP processors. Specifically, we have successfully mined 335 instructions on 14 TI DSP processors from 6 different series.

Citation Download Citation

Xingcan Zhang, Jian Wang, Changlong Liu, and Bin Li "An efficient mining method for DSP undocumented instruction", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 1271816 (1 June 2023); https://doi.org/10.1117/12.2681569

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