2026-09-11T06:09:52Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/112732026-03-16T09:20:25Zcom_10259_3847com_10259_5086com_10259_2604col_10259_8557col_10259_5684

00925njm 22002777a 4500 dc Martínez González, Branly Alberto author Cambra Baseca, Carlos author Urda Muñoz, Daniel author Rincón Arango, Jaime Andrés author Herrero Cosío, Álvaro author 2026-01-23 This study presents a labelled flow-based network traffic dataset collected from a controlled Internet of Things (IoT) laboratory environment. The dataset captures network communication generated by hardware-based IoT devices during normal operation, including MQTT messaging, database synchronization, and web-based monitoring, as well as during the execution of predefined cyber-attack scenarios within an isolated experimental network. Network traffic was recorded at the packet level using passive network monitoring and stored in PCAP format. The packet captures were subsequently processed into bidirectional network flows using a flow-based traffic extraction pipeline, producing flow records with statistical and temporal attributes derived from the observed packet exchanges. Cyberattack-related flows were identified based on predefined attack execution time intervals obtained from experimental metadata. Network flows observed outside these intervals were labelled as benign and correspond to regular device communication. The dataset is distributed through a structured repository that includes raw packet captures, processed flow-level datasets in tabular format, and metadata files describing the experimental setup, attack scenarios, and labelling criteria. The data support flow-based analysis of IoT network traffic and the evaluation of cyberattack detection methods. Martinez Gonzalez, B. A., Cambra, C., Urda Muñoz, D., Rincon Arango, J. A., & Herrero, A. (2026). Labelled IoT Flow-Based Network Traffic Dataset for Cyberattack Detection (Version 1.0) [Data set]. Universidad de Burgos. https://doi.org/10.71486/DYXT-2R24 https://hdl.handle.net/10259/11273 10.71486/dyxt-2r24 https://ss3.scayle.es:443/riubu-1/GICAP/UBUGICAP_AI4SECIOT_v1-2026.zip Internet of Things Cybersecurity Flow features Attack traffic Benign traffic Network Ground truth Labelling CyberFlowIoT-GICAP: Labelled Flow-Based Network Traffic Dataset for Cyberattack Detection [Dataset]