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SensorGuard-RV: Sensor-to-Register Data Protection for Resource-Constrained RISC-V Devices
arXiv cs.CR0 views
arXiv:2611.08380v1 Announce Type: new Abstract: Embedded systems rely on sensor measurements that traverse a complex hardware-firmware stack (e.g. peripheral interfaces, communication buses, memory, and drivers) before reaching the applications that use them. At worst, an attacker with access to this path may successfully intercept, overwrite, fabricate, or replay readings. This may cause systems that act on such data, such as flight controllers and industrial processes, to behave incorrectly or unsafely. Existing defences, such as trusted execution environments and trusted I/O paths, still depend on trusted driver software and correctly configured access control, which remain fragile in practice. In response, we present SensorGuard-RV, a hardware-first approach that delivers sensor samples into processor registers without trusting intermediate software, buses, or memory. After configuration, a write-once ownership switch hands the sensor to a fixed-function controller that performs hardware-level authenticated encryption on sensor samples under a key inaccessible to software. We develop a new RISC-V instruction then instructs the CPU to decrypt the sample and writes it to a register only after verifying its tag and rejecting replays, including across resets. We implement SensorGuard-RV on the NEORV32 core with an ADXL362 SPI accelerometer on an Artix-7 FPGA. At 100 MHz, a protected sample reaches a register in 5,191 cycles (51.9 $\mu$s), of which the two Ascon authenticated encryption engines account for 30. Because fixed-function acquisition removes per-byte software polling, the protected loop sustains 19,015 transactions/s, 5.3% above a software-driven SPI baseline. The added logic (3,155 LUTs and 2,215 registers; 5.0% and 1.7% of the device) is dominated by the Ascon engine and preserves the 100 MHz clock, while estimated power rises from 118 to 368 mW.
Source:https://arxiv.org/abs/2611.08380PreviousLABBA: LLM-Assisted Behavioral Biometric AuthenticationNextFedReason: Large Language Models as Weight-Space Meta-Aggregators for Heterogeneous Federated Learning
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