Smart speakers and voice-based virtual assistants are core components for the success of the IoT paradigm. Unfortunately, they are vulnerable to various privacy threats exploiting machine learning to analyze the generated encrypted traffic. To cope with that, deep adversarial learning approaches can be used to build black-box countermeasures altering the network traffic (e.g., via packet padding) and its statistical information.

Instructions: 

This dataset contains several pcap files generated by the Google Home smart speaker placed under different conditions.

  • Mic_on_off_8h contains two pcap files generated by keeping the microphone on (with silence) and off for 8 hours respectively.
  • Mic_on_off_gquic_8h contains two pcap files generated by keeping the microphone on (with silence) and off for 8 hours respectively, excluding all network traffic not belonging to the google: gquic protocol.
  • Mic_on_off_noise_3d contains three pcap files generated by holding on (with silence), off, and on (with noise) the microphone respectively for 3 days.
  • Mic_on_off_noise_gquic_3d contains three pcap files generated by holding on (with silence), off, and on (with noise) the microphone respectively for 3 days. excluding all network traffic not belonging to the google protocol: gquic.
  • media_pcap_anonymized contains several pcap files after the execution of queries such as "Whats' the latest news?" or "Play some music" (On each file has been stored network traffic collected after the execution of one query).
  • travel_pcap_anonymized contains several pcap files after the execution of queries such as "How is the weather today?" (On each file has been stored network traffic collected after the execution of one query).
  • utilities_pcap_anonymized contains several pcap files after the execution of queries such as "What's on my agenda today?" or "What time is it?" (On each file has been stored network traffic collected after the execution of one query).
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209 Views

The dataset is collected for the purpose of investigating how brainwave signals can be used to industrial insider threat detection. The dataset was connected using Emotiv Insight 5 channels device. The dataset contains data from 17 subjects who accepted to participate in this data collection.

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420 Views

This survey covers more than 150 published papers related to sub-6 GHz wideband LNAs from IEEE publications such as ISSCC, JSSC, TMTT, RFIC, MWCL, TCAS and NEWCAS published in the last 20 years. The considered LNAs are classified according to the technology node and its topology. The presented database is a useful tool for investigating technology trends and comparing the performance of common LNA design styles. 

Instructions: 

- The database is organized by technology and topology. 

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120 Views

We conduct to our knowledge a first measurement study of commercial 5G performance on smartphones by closely examining 5G networks of three carriers (two mmWave carriers, one mid-band 5G carrier) in three U.S. cities. We conduct extensive field tests on 5G performance in diverse urban environments. We systematically analyze the handoff mechanisms in 5G and their impact on network performance, and explore the feasibility of using location and possibly other environmental information to predict the network performance.

Instructions: 

DATASET WEBSITE: https://fivegophers.umn.edu/www20/

## OVERVIEW

5Gophers 1.0 is a dataset collected when the world's very first commercial 5G services were made available to consumers. It should serve as a baseline to evaluate the 5G's performance evolution over time. Results using this dataset is presented in our measurement paper - "A First Look at Commercial 5G Performance on Smartphones".

This dataset is being made available to the research community.

## FILES and FOLDER STRUCTURE

All the files are in CSV format with headers that should hopefully be self-explainatory.

5Gophers-v1.0
├── All-Carriers
│   ├── 01-Throughput
│   ├── 02-Round-Trip-Times
│   └── 03-User-Mobility
└── mmWave-only
├── 03-UE-Panel (LoS Tests)
├── 04-Ping-Traces (Latency Tests)
├── 05-UE-Panel (NLoS Tests)
├── 06-UE-Panel (Orientation Tests)
├── 07-UE-Panel (Distance Tests)
├── 08-Web-Page-Load-Tests
├── 09-HTTPS-CDN-vs-NonCDN (Download Test)
└── 10-HTTP-vs-HTTPS (Download Test)

## CITING THE DATASET

```
@inproceedings{10.1145/3366423.3380169,
author = {Narayanan, Arvind and Ramadan, Eman and Carpenter, Jason and Liu, Qingxu and Liu, Yu and Qian, Feng and Zhang, Zhi-Li},
title = {A First Look at Commercial 5G Performance on Smartphones},
year = {2020},
isbn = {9781450370233},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3366423.3380169},
doi = {10.1145/3366423.3380169},
booktitle = {Proceedings of The Web Conference 2020},
pages = {894–905},
numpages = {12},
location = {Taipei, Taiwan},
series = {WWW ’20}
}
```

## QUESTIONS?

Please feel free to contact the FiveGophers team for information about the data (fivegophers@umn.edu, naray111@umn.edu)

## LICENSE

5Gophers 1.0 dataset is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

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142 Views

The boring and repetitive task of monitoring video feeds makes real-time anomaly detection tasks difficult for humans. Hence, crimes are usually detected hours or days after the occurrence. To mitigate this, the research community proposes the use of a deep learning-based anomaly detection model (ADM) for automating the monitoring process.

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291 Views

This datset contains 2000  images of size 256 X256. The dataset is created by captuirng photos using mobile phone. This dataset is applicable for two classes namely water and wet surface.

Instructions: 

This dataset can be used for two classes such as water and wet surface.

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294 Views

We propose a blockchain-enabled zero trust information sharing protocol. The proposed protocol supports the filtering of fabricated information, and protect participant privacy during information sharing. We then evaluate its performance using a series of experiments. 

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58 Views

This data set is the result of model test trained on the basis of the Stanford earthquake dataset (stead): a global data set of seismic signals for AI, which can effectively get the seismic signal and the arrival time of seismic phase from the image, so as to prove the effectiveness of this model

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149 Views

The Widar3.0 project is a large dataset designed for use in WiFi-based hand gesture recognition. The RF data are collected from commodity WiFi NICs in the form of Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). The dataset consists of 258K instances of hand gestures with a duration of totally 8,620 minutes and from 75 domains. In addition, two sophisticated features from raw RF signal, including Doppler Frequency Shift (DFS) and a new feature Body-coordinate Velocity Profile (BVP) are included.

Instructions: 

Please refer to the README document.

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1523 Views

Open dataset from Machine Learning Repository of Center for Machine Learning and Intelligent Systems at the University of California, Irvine.

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239 Views

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