My(o) Armband Leaks Passwords: An EMG and IMU Based Keylogging Side-Channel Attack

Wearables that constantly collect various sensor data of their users increase the chances for inferences of unintentional and sensitive information such as passwords typed on a physical keyboard. We take a thorough look at the potential of using electromyographic (EMG) data, a sensor modality which is new to the market but has lately gained attention in the context of wearables for augmented reality (AR), for a keylogging side-channel attack. Our approach is based on neural networks for a between-subject attack in a realistic scenario using the Myo Armband to collect the sensor data. In our approach, the EMG data has proven to be the most prominent source of information compared to the accelerometer and gyroscope, increasing the keystroke detection performance. For our end-to-end approach on raw data, we report a mean balanced accuracy of about 76 % for the keystroke detection and a mean top-3 key accuracy of about 32 % on 52 classes for the key identification on passwords of varying strengths. We have created an extensive dataset including more than 310 000 keystrokes recorded from 37 volunteers, which is available as open access along with the source code used to create the given results

Implementation of a Customisable Readout Sequence for the ALICE ITS Upgrade Explorer Family Chips

Within the ALICE ITS upgrade R&D programme the Explorer family chips are developed featuring 11700 pixels which are split into 18 different sectors with different properties. These pixels are read out sequentially leading to a time span of 2.34ms between the first and last pixel. Due to the long readout time, shot noise induced by the leakage currents in the in-pixel analogue memories makes the comparison of different sensor implementations located in distant sectors on the Explorer family chips difficult. In order to reduce this noise contribution a customisable readout sequence is developed to read parts instead of the whole chip which reduces the overall readout time. This readout sequence is integrated in the existing characterisation framework in order to choose the best performing sensor implementation through pixel-by-pixel comparison without readout-induced effects