IoT
The dataset attached is recordings done for 5 parameters to ascertain physical soil composition. Data was collected between March 2021 and April 2021. This dataset is the raw data.
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We generated attack datasets 1 based on real data from Austin, Texas.
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The Development of an Internet of Things (IoT) Network Traffic Dataset with Simulated Attack Data.
Abstract— This research focuses on the requirements for and the creation of an intrusion detection system (IDS) dataset for an Internet of Things (IoT) network domain.
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This dataset is a supplementary material for paper "A Comprehensive and Reproducible Comparison of Cryptographic Primitives Execution on Android Devices" with the measurements collected from 17 mobile devices and the code for reproducibility.
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This dataset comprises sensory data of in and out miniature vehicle (mobile sink) movement in the agriculture fields. The dataset is collected from the miniature vehicle using a 9-axis Inertial Measurement Unit (IMU) sensor (MPU-9250) placed on the top of the vehicle. Though the vehicle is small but designed to handle all the hurdles of the agricultural land, such as rough and muddy surface. This dataset aims to facilitate appropriate path planning in the agricultural field for the automatic cultivation of seeds, manure spread, and nutrients insertion.
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The provided dataset computes the exact analytical bit error rate (BER) of the NOMA system in the SISO broadcast channels with the assumption of i.i.d Rayleigh fading channels. The reader has to decide on the following input: 1) Number of users. 2) Modulation orders. 3) Power assignment. 4) Pathloss. 5) Transmit signal-to-noise ratio (SNR). The output is stored in a matrix where different rows are for different users while different columns are for different transmit SNRs.
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Another raw ADS-B signal dataset with labels, the dataset is captured using a BladeRF2 SDR receiver @ 1090MHz with a sample rate of 10MHz
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In order to obtain the constants of our PID temperature controller, it was necessary to identify the system. The identification of the system allows us, through experimentation, to find the representation of the plant to be able to control it.
The first data with name "data_2.mat" represent the open loop test, where the sampling frequency is 100 [Hz], this data was useful to find the period of the pulse train generator, which is twice the slowest sampling time analyzed between the high pulse and low pulse of the input.
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Dataset with diverse type of attacks in Programmable Logic Controllers:
1- Denial of Service
- Flooding
- Amplification/Volumetric
2- Man in the Middle
The full documentation of the dataset is available at: https://arxiv.org/abs/2103.09380
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2970 Views