Data

UQ IoT IDS dataset 2021

The University of Queensland
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/17b44bb&rft.title=UQ IoT IDS dataset 2021&rft.identifier=10.48610/17b44bb&rft.publisher=The University of Queensland&rft.description=UQ-IOT-IDS-2021 is a dataset focused on cybersecurity in the Internet of Things (IoT) provided by the University of Queensland (UQ), Australia. The dataset contains benign traffic of IoT devices, including smartphones, smart TV, IP cameras, smart speakers, Raspberry Pis, etc., and malicious traffic from 9 types of cyberattacks, including Host Discovery, Port Scanning, Service Detection, ARP Spoofing, Telnet Brute-force, SYN Flooding, UDP Flooding, HTTP Flooding, and ACK Flooding. The raw data was captured with WireShark in a realistic network environment in the School of Information Technology and Electrical Engineering at the University of Queensland. The raw data was processed by the feature extractor in Kitsune(Mirsky et al., 2018) to extract 100 temporal-statistical features. The raw data is saved in pcap files.  The total number of records is 41404983. The dataset consists of Benign Samples and Attack Samples. Benign Samples contain 7 files with only normal traffic that mimic IoT devices' normal daily activities for a whole week (5-weekday samples, 1 Saturday sample, and 1 Sunday sample). Attack Samples were captured separately for each device type under each attack type.&rft.creator=Anonymous&rft.date=2022&rft_subject=cyber security&rft_subject=internet of things&rft.type=dataset&rft.language=English Access the data

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2022, The University of Queensland

Open Access

Permitted Re-use with Acknowledgement

Contact Information

dan.kim@uq.edu.au

Full description

UQ-IOT-IDS-2021 is a dataset focused on cybersecurity in the Internet of Things (IoT) provided by the University of Queensland (UQ), Australia. The dataset contains benign traffic of IoT devices, including smartphones, smart TV, IP cameras, smart speakers, Raspberry Pis, etc., and malicious traffic from 9 types of cyberattacks, including Host Discovery, Port Scanning, Service Detection, ARP Spoofing, Telnet Brute-force, SYN Flooding, UDP Flooding, HTTP Flooding, and ACK Flooding. The raw data was captured with WireShark in a realistic network environment in the School of Information Technology and Electrical Engineering at the University of Queensland. The raw data was processed by the feature extractor in Kitsune(Mirsky et al., 2018) to extract 100 temporal-statistical features. The raw data is saved in pcap files.  The total number of records is 41404983. The dataset consists of Benign Samples and Attack Samples. Benign Samples contain 7 files with only normal traffic that mimic IoT devices' normal daily activities for a whole week (5-weekday samples, 1 Saturday sample, and 1 Sunday sample). Attack Samples were captured separately for each device type under each attack type.

Issued: 15 09 2022

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