IoT Network Intrusion Dataset

The exponential growth of the Internet of Things (IoT) devices provides a large attack surface for intruders to launch more destructive cyber-attacks. The intruder aimed to exhaust the target IoT network resources with malicious activity. New techniques and detection algorithms required a well-designed dataset for IoT networks. We proposed a new dataset, namely IoTID20, generated dataset from [1]. The new IoT botnet dataset has a more comprehensive network and flow-based features. The flow-based feature can be used to analyze and evaluate a flow-based intrusion detection system. Our proposed IoT botnet dataset will provide a reference point to identify anomalous activity across the IoT networks. The IoT Botnet dataset can be accessed from [2]. The new IoTID20 dataset will provide a foundation for the development of new intrusion detection techniques in IoT networks.<br>

物联网（Internet of Things, IoT）设备的指数级增长，为攻击者提供了庞大的攻击面，使其能够发起更具破坏性的网络攻击。攻击者旨在通过恶意活动耗尽目标物联网网络的资源。新型检测技术与算法亟需设计精良的物联网网络数据集。本研究提出一款全新数据集IoTID20，其生成自参考文献[1]。这款新型物联网僵尸网络数据集涵盖了更全面的网络特征与基于流的特征，其中基于流的特征可用于分析与评估基于流的入侵检测系统。本研究提出的物联网僵尸网络数据集，可为物联网网络中的异常活动识别提供参考基准。该物联网僵尸网络数据集可通过参考文献[2]获取。全新的IoTID20数据集，将为物联网网络中新型入侵检测技术的研发奠定基础。