Introduction
Over the past month, our team has continued to work towards our end goal of collecting, sending, and tracking Bluetooth devices. We have continued to organize processes and tools we need to record Bluetooth data so it can be analyzed. We have come much closer to reaching our end goal. We’ve had quite a few challenges along the way, and have worked hard to overcome them.
Collecting Data
The automation process of this project is crucial. If we don’t have a concrete automation process, analyzed data could include false or duplicate information. Our self-built Python script helped us complete our automation tasks. The script handles searching and collecting data about Bluetooth devices in range. It also parses out all the information regarding our discoveries, and sends it to our Elasticsearch server. We chose to use Python because we could code exactly what we wanted, and could also take advantage of the Elasticsearch Python library.
The Challenges
During the past month we’ve faced quite a few challenges when writing our Python script. Automating our processes took a lot of time. Our team-work allowed us to collaborate, share code, and reach our goal faster. Our first issue was finding a solution to starting and stopping Blue Hydra, an open-source Bluetooth library by PwnieExpress. All collected data also needed to be sent to our Elasticsearch server. Running Blue Hydra automatically was one issue. Sending our collected data to Elasticsearch was another.
Our team has basic knowledge and experience using Elasticsearch. This made adding information to our server difficult. After many days of tinkering and playing, we were able to get the script working properly. And SpyPies.py was born to the Pi family. Hopefully it fits in!
Testing and Further Developments
Our project has come a long way over one month. There is definitely a foreseeable future for this project in terms of practicality and real-world use. Our team is currently in the process of testing to check for bugs or errors with how our data is being stored. We want to make sure this project can be expanded to add more nodes in the future, and making sure our recent work is fully functional is important for this. We plan on testing and writing much more about this project in the near future. Stay tuned to see some new advancements in our following blog post!
Post any feedback, questions, or general comments in the comment section below! Interested in our research? Follow the Leahy Center for Digital Investigation (LCDI) on Twitter @ChampForensics, Instagram @ChampForensics and Facebook @ChamplainLCDI.