物联网中的安全性和机器学习采用：物联网开发人员讨论的初步研究（CS）

物联网（IoT）定义为通过智能计算设备通过互联网/网络在位置和物理对象（即事物）之间建立的连接。传统上讲，我们通过对物联网开发人员/从业人员进行调查来了解物联网生态系统/问题。另一种了解的方法是通过分析流行的在线开发人员论坛（例如Stack Overflow（SO））中的IoT开发人员讨论。但是，我们明白尚无此类研究专注于IoT开发人员的安全性以及SO中与ML相关的讨论。本文将会提供SO中IoT开发人员讨论的初步研究结果。我们发现大约12％的句子包含安全性的讨论，而大约0.12％的句子包含ML相关的讨论。我们发现IoT开发人员经常讨论安全性问题，询问如何在IoT设备和用户之间安全地存储，共享和传输共享数据。我们还发现，物联网开发人员有在其IoT设备中采用基于深度神经网络的ML模型很感兴趣，但他们也发现将这些模型容纳到资源受限的IoT设备中具有挑战性。我们的发现能为物联网供应商和研究人员在开发和设计新颖的技术以改善物联网设备的安全性和机器学习的使用提供了启示。

原文题目：Security and Machine Learning Adoption in IoT: A Preliminary Study of IoT Developer Discussions

原文：Internet of Things (IoT) is defined as the connection between places and physical objects (i.e., things) over the internet/network via smart computing devices. Traditionally, we learn about the IoT ecosystem/problems by conducting surveys of IoT developers/practitioners. Another way to learn is by analyzing IoT developer discussions in popular online developer forums like Stack Overflow (SO). However, we are aware of no such studies that focused on IoT developers' security and ML-related discussions in SO. This paper offers the results of preliminary study of IoT developer discussions in SO. We find around 12% of sentences contain security discussions, while around 0.12% sentences contain ML- related discussions. We find that IoT developers discussing security issues frequently inquired about how the shared data can be stored, shared, and transferred securely across IoT devices and users. We also find that IoT developers are interested to adopt deep neural network-based ML models into their IoT devices, but they find it challenging to accommodate those into their resource-constrained IoT devices. Our findings offer implications for IoT vendors and researchers to develop and design novel techniques for improved security and ML adoption into IoT devices.