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Information Exposure From Consumer IoT Devices

This site contains analysis code accompanying the paper "Information Exposure From Consumer IoT Devices: A Multidimensional, Network-Informed Measurement Approach" in proceedings of the ACM Internet Measurement Conference 2019 (IMC 2019), October 2019, Amsterdam, Netherlands.

The official paper can be found at The site also contains instructions for requesting access to the full dataset.

The testbed code and documentation can be found at Currently, it is deployed at both Northeastern University and Imperial College London.

GitHub Logo

Figure 1: The IoT Lab at Northeastern University.

File Structure

Each subdirectory shows samples for processing pcap files for destination, encryption, and content analysis.

  • destination/ - Code for Section 4 Destination Analysis - analyze the destinations that traffic is being sent to and received from.
  • encryption/ - Code for Section 5 Encryption Analysis - analyze whether traffic is encrypted or unencrypted.
  • model/ - Code for Section 6 Content Analysis - create machine learning models to predict the state of an IoT device using its network traffic.
  • moniotr/ - Code to automate experiments.
  • - A step-by-step tutorial to get started analyzing data using each of the three analyses.
  • - The license for this software.
  • - This file. Contains an overview of the software.
  • lab.png - A photo of the IoT Lab at Northeastern University.


We release the traffic (packet headers) from 34,586 controlled experiments and 112 hours of idle IoT traffic.

The naming convention for the data is {country}{-vpn}/{device_name}/{activity_name}/{datetime}.{length}.pcap. For example, us/amcrest-cam-wired/power/2019-04-10_21:32:18.256s.pcap is the traffic collected from device amcrest-cam-wired when power on at the time of 2019-04-10_21:32:18, which lasts 256 seconds in the us lab without VPN.

To obtain access to the dataset, please follow the instructions on the paper webpage at We require that you agree to the terms of our data sharing agreement. This is out of an abundance of caution to protect any private or security-sensitive information that we were unable to remove from the traces.


This version relies on Python 3.6 (tested on Python 3.6.9).

It is strongly suggested that one uses the following virtual environment:

sudo apt-get install virtualenv
sudo apt-get install libpcap-dev libpq-dev
sudo apt-get install python3-dev
sudo apt-get install python3.6-tk
sudo apt-get install gcc
sudo apt-get install tshark

virtualenv -p python3.6 env
source env/bin/activate

Once the environment is setup and running, install the following packages:

pip install numpy
pip install scipy
pip install pyshark
pip install geoip2
pip install matplotlib
pip install dpkt
pip install pycrypto
pip install IPy
pip install pcapy
pip install scapy
pip install Impacket
pip install mysql-connector-python-rf
pip install pandas
pip install tldextract
pip install python-whois
pip install ipwhois
pip install psutil

For more information about the pipelines and the contents of the code, see the READMEs for destination analysis, encryption analysis, and content analysis. Content analysis also has a page describing the machine learning models and contents of that directory in depth: model/

For step-by-step instructions to get started analyzing data, see


Datasets and code for IMC'19 paper on information exposure from IoT devices







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