Dataset for IoT Local (IMC23)

In the Room Where It Happens: Characterizing Local Communication and Threats in Smart Homes

Aniketh Girish*, Tianrui Hu*, Vijay Prakash, Daniel J. Dubois, Srdjan Matic, Danny Yuxing Huang, Serge Egelman, Joel Reardon, Juan Tapiador, David Choffnes, and Narseo Vallina-Rodriguez.

*equal contribution 

This request is for the MonIoTr Lab Testbed datasets, i.e., IoT local traffic (MonIoTr Lab passive dataset), IoT honeypot data, active scan data, and App-to-IoT local traffic data. For mobile app analysis data and IoT Inspector dataset, please request them here.

Data Sharing Agreement for Accessing the Dataset

1. LICENSE. You are given a non-exclusive and non-transferable license to access and use the dataset for the purpose of non-profit research and education.
2. NON-DISCLOSURE. You will not disclose the dataset to any person other than those employed by your institute who are assisting or collaborating with you using the dataset. Other entities must request access to the dataset separately by sending a request to moniotr@ccs.neu.edu.
3. MANDATORY CITATION. If you create a publication (including web pages, papers published by a third party, teaching material, and publicly available presentations) using data from this dataset, you must cite our paper as follows:

   Title: In the Room Where It Happens: Characterizing Local Communication and Threats in Smart Homes
   Authors: Aniketh Girish, Tianrui Hu, Vijay Prakash, Daniel J. Dubois, Srdjan Matic, Danny Yuxing Huang, Serge Egelman, Joel Reardon, Juan Tapiador, David Choffnes, and Narseo Vallina-Rodriguez.
   Venue: Internet Measurement Conference (IMC) 2023
   

   Or, in bibtex format:

   @inproceedings{girish-imc23,
   title={{In the Room Where It Happens: Characterizing Local Communication and Threats in Smart Homes}},
   author={Girish, Aniketh and Hu, Tianrui and Prakash, Vijay and Dubois, Daniel J. and Matic, Srdjan and Yuxing, Danny and Egelman, Serge and Reardon, Joel and Tapiador, Juan and Choffnes, David and Vallina-Rodriguez, Narseo},
   booktitle={Proc. of the Internet Measurement Conference (IMC)},
   year={2023}
   }

4. CONFIDENTIALITY. All data from this dataset is confidential. You agree to protect the confidentiality of the data and to prevent its unauthorized disclosure and use.
5. ANONYMIZATION. For any publication or other disclosure, you will anonymize or de-identify any credentials, authentication tokens, unique identifiers, and any other personally identifiable information you find in the dataset.
6. NO ABUSE. You will not attempt to use any information that can be derived from the dataset for purposes that are different from non-profit research and education. This includes disclosing any form of credentials or personally identifiable information found in the dataset, or using them for the purpose of gaining unauthorized access to any third-party services or systems. We have done our best to ensure that the dataset contains no data that could be used to compromise our systems; however, if you find any vulnerabilities or credentials in the dataset, you must responsibly disclose them to us or the manufacturers of systems affected by them.

If you agree to those terms, send an email to the Mon(IoT)r research group at moniotr@ccs.neu.edu with subject “IMC 2023 IoT Local Dataset.” In the body of the email, you must state that you have read our agreement and that you agree to abide by its terms. Please be sure to include your name and affiliation in your email as well. We are asking this because, despite our best efforts to anonymize the data, there can still be private or security-sensitive information that we were unable to remove from the traces.