Fatemeh Khojasteh Dana, Saleh Khalaj Monfared, Shahin Tajik (Worcester Polytechnic Institute)

Satellites are highly vulnerable to adversarial glitches or high-energy radiation in space, which could cause faults on the onboard computer. Various radiation- and fault-tolerant methods, such as error correction codes (ECC) and redundancybased approaches, have been explored over the last decades to mitigate temporary soft errors on software and hardware. However, conventional ECC methods fail to deal with hard errors or permanent faults in the hardware components. This work introduces a detection- and response-based countermeasure to deal with partially damaged processor chips. It recovers the processor chip from permanent faults and enables continuous operation with available undamaged resources on the chip. We incorporate digitally-compatible delay-based sensors on the target processor’s chip to reliably detect the incoming radiation or glitching attempts on the physical fabric of the chip, even before a fault occurs. Upon detecting a fault in one or more components of the processor’s arithmetic logic unit (ALU), our countermeasure employs adaptive software recompilations to resynthesize and substitute the affected instructions with instructions of still functioning components to accomplish the task. Furthermore, if the fault is more widespread and prevents the correct operation of the entire processor, our approach deploys adaptive hardware partial reconfigurations to replace and reroute the failed components to undamaged locations of the chip. To validate our claims, we deploy a high-energy nearinfrared (NIR) laser beam on a RISC-V processor implemented on a 28 nm FPGA to emulate radiation and even hard errors by partially damaging the FPGA fabric. We demonstrate that our sensor can confidently detect the radiation and trigger the processor testing and fault recovery mechanisms. Finally, we discuss the overhead imposed by our countermeasure.

View More Papers

Rethink Custom Transformers for Binary Analysis

Heng Yin, Professor, Department of Computer Science and Engineering, University of California, Riverside

Read More

URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning

Duanyi Yao (Hong Kong University of Science and Technology), Songze Li (Southeast University), Xueluan Gong (Wuhan University), Sizai Hou (Hong Kong University of Science and Technology), Gaoning Pan (Hangzhou Dianzi University)

Read More

User Comprehension and Comfort with Eye-Tracking and Hand-Tracking Permissions...

Kaiming Cheng (University of Washington), Mattea Sim (Indiana University), Tadayoshi Kohno (University of Washington), Franziska Roesner (University of Washington)

Read More