标题： 一种用于去中心化联邦学习模型的众包感知入侵检测数据集 显示英文标题

标题： A Crowdsensing Intrusion Detection Dataset For Decentralized Federated Learning Models

摘要： 本文介绍了用于物联网众包感知恶意软件检测的去中心化联邦学习（DFL）的数据集和实验研究。 该数据集包含良性程序和八个恶意软件家族的行为记录。 总共收集了21,582,484条原始记录，来自系统调用、文件系统活动、资源使用、内核事件、输入/输出事件和网络记录。 这些记录被聚合为30秒的时间窗口， resulting in 342,106个特征用于模型训练和评估。 在DFL平台上进行的实验比较了传统机器学习（ML）、集中式联邦学习（CFL）和DFL在不同节点数、拓扑结构和数据分布下的表现。 结果表明，DFL在保持数据本地性的同时保持了竞争力，在大多数设置中优于CFL。 这个数据集为研究物联网众包感知环境的安全性提供了坚实的基础。 显示英文摘要

摘要： This paper introduces a dataset and experimental study for decentralized federated learning (DFL) applied to IoT crowdsensing malware detection. The dataset comprises behavioral records from benign and eight malware families. A total of 21,582,484 original records were collected from system calls, file system activities, resource usage, kernel events, input/output events, and network records. These records were aggregated into 30-second windows, resulting in 342,106 features used for model training and evaluation. Experiments on the DFL platform compare traditional machine learning (ML), centralized federated learning (CFL), and DFL across different node counts, topologies, and data distributions. Results show that DFL maintains competitive performance while preserving data locality, outperforming CFL in most settings. This dataset provides a solid foundation for studying the security of IoT crowdsensing environments.