TriSweep: 一种四无人机群框架用于电磁侧信道分析
TriSweep: A Four-Drone Swarm Framework for Electromagnetic Side-Channel Analysis
AI总结：
本文提出TriSweep框架，通过四无人机群实现自主远距离电磁侧信道分析，针对嵌入式微控制器在0.25-1.5米范围内进行攻击，通过空间专业化收集无人机和固定积累无人机的协同工作，实现信号增强和掩码消除，验证了无人机群在对抗环境中的有效性。
摘要：
电磁（EM）侧信道分析传统上假设存在一个静止且近距离的探测器，这种威胁模型低估了空中对手的威胁。TriSweep是一种模拟框架，设计并评估了一种四无人机群架构，用于自主远距离电磁侧信道分析（EM-SCA）嵌入式微控制器，距离为0.25-1.5米。三个空间专业化收集无人机——锚点（全频谱）、掩码探测器（掩码寄存器加载泄漏）和密码探测器（掩码SubBytes输出泄漏）——将信号馈入一个固定积累无人机，该无人机通过两个空间分离泄漏流的中心乘积进行相干结合（+4.8 dB信噪比增益）和二次掩码消除。在三个真实的ANSSI ASCAD数据集（ATmega8515掩码AES-128和50/100样本非同步变体）上评估该框架，其在0.25米范围内主要掩码数据集上实现了模拟密钥排名为18±1.7（五种子）。通过轮廓跟踪轨迹交叉相关对齐，单无人机排名从89降低到21，在100样本抖动变体上展示了对无人机悬停振动的补偿。积累无人机中的两个通道CNN收敛到损失为0.454（与随机基线5.545相比）并在非同步数据集上提高了排名。尚未制造物理硬件；原型构建是下一步计划。
英文摘要：
Electromagnetic (EM) side-channel analysis traditionally assumes a stationary, close-proximity probe - a threat model that underestimates aerial adversaries. TriSweep is a simulation framework that designs and evaluates a four-drone swarm architecture for autonomous standoff EM-SCA of embedded microcontrollers at 0.25-1.5 m. Three spatially specialized collector drones - Anchor (full-spectrum), Mask Probe (mask-register loading leakage), and Cipher Probe (masked SubBytes output leakage) - feed a stationary Accumulator drone that performs coherent combining (+4.8 dB SNR gain) and second-order mask cancellation via a centered product of the two spatially separated leakage streams. Evaluated against three real ANSSI ASCAD datasets (ATmega8515 masked AES-128 and 50/100-sample desynchronized variants), the framework achieves a simulated key rank of 18 +/- 1.7 (five-seed) at 0.25 m on the primary masked dataset. Profiling-trace cross-correlation alignment reduces single-drone rank from 89 to 21 on the 100-sample-jitter variant, demonstrating compensation for drone hover vibration. A two-channel CNN in the Accumulator converges to a loss of 0.454 (vs. random baseline 5.545) and improves rank on desynchronized datasets. No physical hardware has been fabricated; prototype construction is the planned next step.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
