Status Report 10 – Team A2: Project LAKE – Logging of Acoustic Keyboard Emanations

Kevin

- - - - The new PCBs and parts came in this week. We assembled and tested all of them. There appear to be no major issues with any of the assembled boards. We were able to flash all of them successfully. Additionally, we can power them via batteries and charge the batteries in the manor intended. Both microphones are are functioning properly and we are receiving good audio quality.
      - The does appear to be a minor issue with the battery management unit however, on two of the board when powered only through the 5V pin, the output voltage sometimes drops below 3.3V, causing the ESP32 to brown-out. The issue is likely caused by imperfect solder joints, however it not not severe enough to warrant re-work.
      - I tested the current draw of the board during the different power modes. The board is pulling about 120mA when in normal use and 0.7mA when in deep sleep mode.

- - Next week we will be giving our final presentation, given by James. We are finishing up the Final Presentation and practicing the talk.
  - We will mostly be focused on getting ready for the demo; practicing how everything will work during the live demo.

- - This week, I worked toward improving the machine learning algorithms toward labeled data, as we were unsuccessful in fully eliminating dropped data.
  - By using labeled data, I was able to achieve a leave-one-out cross validation accuracy of 16%, with a sample size of 1107.
  - Moving forward, using this trained classifier, unlabeled 10-character random passwords were able to be retrieved with fairly decent accuracy. We met our original requirement of guessing 80% of 10-character random passwords in 75 tries or less. We also achieved a successful guess rate of 50% in 5 tries or less, and 40% in 1 try.

In this next week, we will need to work toward preparing for the demo. I will further tune the parameters the classifier to attempt to improve accuracy. I will construct a sound-proofing box to lower the loud background noise we expect in the gym during the final demo.

The ESP32 is still randomly dropping large chunks of data. As such we have been unable to collect accurate tdoa data.
I have tried switching to the auxiliary peripheral clock, an external, more accurate oscillator on the esp32, however this issue still persists.
For a time we thought that the issue may be because the dma buffers are filling up, we tried increasing the dma buffer and switching to udp. However, this did not seem to fix the issue.
We have so far been unable to ascertain the reason for the dropped data.
I finally tested the esp32’s current draw in deep sleep mode. It come to about 0.7mA. Which is very small compared to its nominal current draw of 0.1-0.3mA during normal data collection and transmission.
At this point we have to rely on frequency features alone. I worked with James to get a fresh new clean set of data. With leave one out cross correlation, we were able to get about a 20-30% error rate.
We generated some confusion matrices and used a breadth first approach to generate the top 75 possible guesses for the password.

I will continue to investigate the dropped data, but my major focus will be on integration and getting a working demo.

- - This week, the team worked closely together to assemble the final revision of the PCB. Three working boards were assembled.
  - Different options were explored to eliminate dropped packets to aid in TDoA localization and clustering. However, because we were unable to, we have decided to move forward with labeled data.
  - We have achieved good accuracy in classification using labeled data and have met our original requirement for password accuracy.

In the next week, we will need to focus on preparing for the final demo.