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

• URL: http://arxiv.org/abs/2112.02382v1
• Date: Sat, 4 Dec 2021 16:48:56 GMT
• Title: My(o) Armband Leaks Passwords: An EMG and IMU Based Keylogging Side-Channel Attack
• Authors: Matthias Gazzari, Annemarie Mattmann, Max Maass, Matthias Hollick
• Abstract summary: We take a look at the potential of using electromyographic (EMG) data, a sensor modality which is new to the market. In our approach, the EMG data has proven to be the most prominent source of information compared to the accelerometer and gyroscope. We have created an extensive dataset including more than 310 000 keystrokes recorded from 37 volunteers.
• Score: 8.195365677760211
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography [47.160223334501126]
  emg2qwerty is a large-scale dataset of non-invasive electromyographic signals recorded at the wrists while touch typing on a QWERTY keyboard. With 1,135 sessions spanning 108 users and 346 hours of recording, this is the largest such public dataset to date. We show strong baseline performance on predicting key-presses using sEMG signals alone.
  arXiv  Detail & Related papers  (2024-10-26T05:18:48Z)
• IEEE BigData 2023 Keystroke Verification Challenge (KVC) [14.366081634293721]
  This paper considers the biometric verification performance of Keystroke Dynamics captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively.
  arXiv  Detail & Related papers  (2024-01-29T20:51:42Z)
• Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation [21.63351064421652]
  Keystroke dynamics (KD) for biometric verification has several advantages. KD is among the most discriminative behavioral traits. We present a new experimental framework to benchmark KD-based biometric verification performance and fairness.
  arXiv  Detail & Related papers  (2023-11-10T11:23:28Z)
• OverHear: Headphone based Multi-sensor Keystroke Inference [1.9915929143641455]
  We develop a keystroke inference framework that leverages both acoustic and accelerometer data from headphones. We achieve top-5 key prediction accuracy of around 80% for mechanical keyboards and around 60% for membrane keyboards. Results highlight the effectiveness and limitations of our approach in the context of real-world scenarios.
  arXiv  Detail & Related papers  (2023-11-04T00:48:20Z)
• Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing [58.73924499067486]
  Blockage detection is achieved by classifying light detection and ranging (LIDAR) data through a physics-based graph neural network (GNN) For precoder design, a preliminary channel estimate is obtained by running ray tracing on a 3D surface obtained from LIDAR data. Numerical simulations show that blockage detection is successful with 95% accuracy.
  arXiv  Detail & Related papers  (2022-09-15T15:04:55Z)
• BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection [63.447493500066045]
  This work proposes a data driven learning model for the synthesis of keystroke biometric data. The proposed method is compared with two statistical approaches based on Universal and User-dependent models. Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects.
  arXiv  Detail & Related papers  (2022-07-27T09:26:15Z)
• Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
  This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits. Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
  arXiv  Detail & Related papers  (2022-03-14T17:05:59Z)
• Spotting adversarial samples for speaker verification by neural vocoders [102.1486475058963]
  We adopt neural vocoders to spot adversarial samples for automatic speaker verification (ASV) We find that the difference between the ASV scores for the original and re-synthesize audio is a good indicator for discrimination between genuine and adversarial samples. Our codes will be made open-source for future works to do comparison.
  arXiv  Detail & Related papers  (2021-07-01T08:58:16Z)
• To augment or not to augment? Data augmentation in user identification based on motion sensors [24.182791316595576]
  In this paper, we study several data augmentation techniques in the quest of finding useful augmentation methods for motion sensor data. We find that data augmentation is not very helpful, most likely because the signal patterns useful to discriminate users are too sensitive to the transformations brought by certain data augmentation techniques.
  arXiv  Detail & Related papers  (2020-09-01T09:11:12Z)
• FAIRS -- Soft Focus Generator and Attention for Robust Object Segmentation from Extreme Points [70.65563691392987]
  We present a new approach to generate object segmentation from user inputs in the form of extreme points and corrective clicks. We demonstrate our method's ability to generate high-quality training data as well as its scalability in incorporating extreme points, guiding clicks, and corrective clicks in a principled manner.
  arXiv  Detail & Related papers  (2020-04-04T22:25:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.