In this paper, the authors introduce a lightweight dataset to interpret IoT (Internet of Things) activity in preparation to create decoys by replicating known data traffic patterns. The dataset comprises different scenarios in a real network setting. This paper also surveys information related to other IoT datasets along with the characteristics that make our data valuable. Many of the datasets available are synthesized (simulated) or often address industrial applications, while the IoT dataset we present is based on likely smart home scenarios. Further, there are only a limited number of IoT datasets that contain both normal operation and attack scenarios. A discussion of the network configuration and the steps taken to prepare this dataset are presented as we prepare to create replicative patterns for decoy purposes. The dataset, which we refer to as IoT Flex Data, consists of four categories, namely, IoT benign idle, IoT benign active, IoT setup, and malicious (attack) traffic associating the IoT devices with the scenarios under consideration.

翻译：本文介绍了一种轻量级数据集，用于解析物联网活动，旨在通过复制已知的数据流量模式来创建诱饵。该数据集包含真实网络环境中的多种场景。本文还综述了其他物联网数据集的相关信息，并阐述了本数据集的价值特征。现有数据集多为合成（模拟）数据或主要面向工业应用，而本文提出的物联网数据集则基于典型的智能家居场景。此外，同时包含正常运行与攻击场景的物联网数据集数量有限。本文详细讨论了网络配置及数据集构建步骤，为生成用于诱饵目的的复制模式奠定基础。该数据集（称为IoT Flex Data）包含四个类别：物联网设备良性空闲状态、良性活跃状态、设备配置阶段，以及与所研究场景相关的恶意（攻击）流量。