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[   ]iot_23_datasets_full.tar.gz2022-03-25 18:20 20G 
[   ]iot_23_datasets_small.tar.gz2022-03-26 00:27 8.7G 

Aposemat IoT-23: A labeled dataset with malicious and benign IoT network traffic.

This IoT network traffic was captured in the Stratosphere Laboratory, AIC group, FEL, CTU University, Czech Republic. Its goal is to offer a large dataset of real and labeled IoT malware infections and IoT benign traffic for researchers to develop machine learning algorithms. This dataset and its research is funded by Avast Software, Prague.

Description

The IoT-23 dataset consists of twenty three captures (called scenarios) of different IoT network traffic. These scenarios are divided into twenty network captures (pcap files) from infected IoT devices and three network captures of real IoT devices network traffic. On each malicious scenario we executed a specific malware in a Raspberry Pi, that used several protocols and performed different actions. The network traffic capture for the bening scenarios was obtained by capturing the network traffic of three different IoT devices: a Philips HUE smart LED lamp, an Amazon Echo home intelligent personal assistant and a Somfy smart doorlock. It is important to mention that these three IoT devices are real hardware and not simulated. This allows us to capture and analyze real network behaviour. Both malicious and benign scenarios run in a controlled network environment with unrestrained internet connection like any other real IoT device.

A more detailed description of the dataset can be found here. The licence of the IoT-23 is CC-BY

The following table summarized each scenario and its basic characteristics. See a more comprehensive detail of each scenario at [IoT-23 Dataset] (https://www.stratosphereips.org/datasets-iot23)

Scenario # Type Capture Name Malware/Device Duration Number of Packets Total Flows Total Size (GB)
Scenario 1 Malicious CTU-IoT-Malware-Capture-34-1 Mirai 24.000 233,000 23,146.000 0.121
Scenario 2 Malicious CTU-IoT-Malware-Capture-43-1 Mirai 1.000 82,000,000 67,321,810.000 6.000
Scenario 3 Malicious CTU-IoT-Malware-Capture-44-1 Mirai 2.000 1,309,000 238.000 1.700
Scenario 4 Malicious CTU-IoT-Malware-Capture-49-1 Mirai 8.000 18,000,000 5,410,562.000 1.300
Scenario 5 Malicious CTU-IoT-Malware-Capture-52-1 Mirai 24.000 64,000,000 19,781,379.000 4.600
Scenario 6 Malicious CTU-IoT-Malware-Capture-20-1 Torii 24.000 50,000 3,210.000 0.004
Scenario 7 Malicious CTU-IoT-Malware-Capture-21-1 Torii 24.000 50,000 3,287.000 0.004
Scenario 8 Malicious CTU-IoT-Malware-Capture-42-1 Trojan 8.000 24,000 4,427.000 0.003
Scenario 9 Malicious CTU-IoT-Malware-Capture-60-1 Gagfyt 24.000 271,000,000 3,581,029.000 21.000
Scenario 10 Malicious CTU-IoT-Malware-Capture-17-1 Kenjiro 24.000 109,000,000 54,659,864.000 7.800
Scenario 11 Malicious CTU-IoT-Malware-Capture-36-1 Okiru 24.000 13,000,000 13,645,107.000 0.992
Scenario 12 Malicious CTU-IoT-Malware-Capture-33-1 Kenjiro 24.000 54,000,000 54,454,592.000 3.900
Scenario 13 Malicious CTU-IoT-Malware-Capture-8-1 Hakai 24.000 23,000 10,404.000 0.002
Scenario 14 Malicious CTU-IoT-Malware-Capture-35-1 Mirai 24.000 46,000,000 10,447,796.000 3.600
Scenario 15 Malicious CTU-IoT-Malware-Capture-48-1 Mirai 24.000 13,000,000 3,394,347.000 1.200
Scenario 16 Malicious CTU-IoT-Malware-Capture-39-1 IRCBot 7.000 73,000,000 73,568,982.000 5.300
Scenario 17 Malicious CTU-IoT-Malware-Capture-7-1 Linux,Mirai 24.000 11,000,000 11,454,723.000 0.897
Scenario 18 Malicious CTU-IoT-Malware-Capture-9-1 Linux.Hajime 24.000 6,437,000 6,378,294.000 0.472
Scenario 19 Malicious CTU-IoT-Malware-Capture-3-1 Muhstik 36.000 496,000 156,104.000 0.056
Scenario 20 Malicious CTU-IoT-Malware-Capture-1-1 Hide and Seek 112.000 1,686,000 1,008,749.000 0.140
Scenario 21 Benign CTU-Honeypot-Capture-7-1 Soomfy Doorlock 1.4 8,276 139.000 0.002
Scenario 22 Benign CTU-Honeypot-Capture-4-1 Phillips HUE 24.000 21,000.000 461.000 0.004
Scenario 23 Benign CTU-Honeypot-Capture-5-1 Amazon Echo 5.400 398,000.000 1,383.000 0.364

Citation

If you are using this dataset for your research, please reference it as “Stratosphere Laboratory. A labeled dataset with malicious and benign IoT network traffic. January 22th. Agustin Parmisano, Sebastian Garcia, Maria Jose Erquiaga. https://www.stratosphereips.org/datasets-iot23

Binaries Zip files

The password of the zip files for the binaries on each scenario is: infected