微型飞行器上的网络安全：一个基于 OpenTitan 的视觉通信用例
Cyber Security aboard Micro Aerial Vehicles: An OpenTitan-based Visual Communication Use Case
AI总结：
针对资源受限微型飞行器的安全漏洞，本文提出集成OpenTitan信任根与并行加速器的开源异构SoC，并通过LED视觉通信用例验证其安全检测与消息重构能力。
摘要：
自主微型飞行器（MAV）的外形尺寸为直径10厘米，凭借其机载智能所带来的广泛适用性，正成为一项新兴技术。然而，这些平台在处理方面的机载功耗范围受到严格限制，即低于几百毫瓦，这使得机载处理器只能局限于简单的微控制器单元（MCU）类别。这些MCU缺乏先进的安全特性，从而为从同一机队中各智能体之间的通信，到机载执行恶意代码等广泛的网络安全漏洞打开了大门。本工作提出了一种开源片上系统（SoC）设计，该设计集成了一个可运行64位Linux的主机处理器，并由一个8核32位并行可编程加速器进行加速。该异构系统架构与一个基于开源OpenTitan信任根的安全飞地相结合。为了演示我们的设计，我们提出了一个用例：OpenTitan检测MAV机载SoC上的安全漏洞，并驱动其专用GPIO以启动LED闪烁例程。该过程体现了两架手掌大小的MAV之间的一种非常规视觉通信：接收方MAV使用运行在并行加速器上的机载卷积神经网络，对发送方的LED状态（亮或灭）进行分类。随后，它在1.3秒内重构出一条高层消息，比当前商用解决方案快2.3倍。
英文摘要：
Autonomous Micro Aerial Vehicles (MAVs), with a form factor of 10cm in diameter, are an emerging technology thanks to the broad applicability enabled by their onboard intelligence. However, these platforms are strongly limited in the onboard power envelope for processing, i.e., less than a few hundred mW, which confines the onboard processors to the class of simple microcontroller units (MCUs). These MCUs lack advanced security features opening the way to a wide range of cyber security vulnerabilities, from the communication between agents of the same fleet to the onboard execution of malicious code. This work presents an open source System on Chip (SoC) design that integrates a 64 bit Linux capable host processor accelerated by an 8 core 32 bit parallel programmable accelerator. The heterogeneous system architecture is coupled with a security enclave based on an open source OpenTitan root of trust. To demonstrate our design, we propose a use case where OpenTitan detects a security breach on the SoC aboard the MAV and drives its exclusive GPIOs to start a LED blinking routine. This procedure embodies an unconventional visual communication between two palm sized MAVs: the receiver MAV classifies the LED state of the sender (on or off) with an onboard convolutional neural network running on the parallel accelerator. Then, it reconstructs a high-level message in 1.3s, 2.3 times faster than current commercial solutions.
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。
