62 research outputs found
DeepWalking: Enabling Smartphone-based Walking Speed Estimation Using Deep Learning
Walking speed estimation is an essential component of mobile apps in various
fields such as fitness, transportation, navigation, and health-care. Most
existing solutions are focused on specialized medical applications that utilize
body-worn motion sensors. These approaches do not serve effectively the general
use case of numerous apps where the user holding a smartphone tries to find his
or her walking speed solely based on smartphone sensors. However, existing
smartphone-based approaches fail to provide acceptable precision for walking
speed estimation. This leads to a question: is it possible to achieve
comparable speed estimation accuracy using a smartphone over wearable sensor
based obtrusive solutions?
We find the answer from advanced neural networks. In this paper, we present
DeepWalking, the first deep learning-based walking speed estimation scheme for
smartphone. A deep convolutional neural network (DCNN) is applied to
automatically identify and extract the most effective features from the
accelerometer and gyroscope data of smartphone and to train the network model
for accurate speed estimation. Experiments are performed with 10 participants
using a treadmill. The average root-mean-squared-error (RMSE) of estimated
walking speed is 0.16m/s which is comparable to the results obtained by
state-of-the-art approaches based on a number of body-worn sensors (i.e., RMSE
of 0.11m/s). The results indicate that a smartphone can be a strong tool for
walking speed estimation if the sensor data are effectively calibrated and
supported by advanced deep learning techniques.Comment: 6 pages, 9 figures, published in IEEE Global Communications
Conference (GLOBECOM
PILOT: Password and PIN Information Leakage from Obfuscated Typing Videos
This paper studies leakage of user passwords and PINs based on observations
of typing feedback on screens or from projectors in the form of masked
characters that indicate keystrokes. To this end, we developed an attack called
Password and Pin Information Leakage from Obfuscated Typing Videos (PILOT). Our
attack extracts inter-keystroke timing information from videos of password
masking characters displayed when users type their password on a computer, or
their PIN at an ATM. We conducted several experiments in various attack
scenarios. Results indicate that, while in some cases leakage is minor, it is
quite substantial in others. By leveraging inter-keystroke timings, PILOT
recovers 8-character alphanumeric passwords in as little as 19 attempts. When
guessing PINs, PILOT significantly improved on both random guessing and the
attack strategy adopted in our prior work [4]. In particular, we were able to
guess about 3% of the PINs within 10 attempts. This corresponds to a 26-fold
improvement compared to random guessing. Our results strongly indicate that
secure password masking GUIs must consider the information leakage identified
in this paper
SaferCross: Enhancing Pedestrian Safety Using Embedded Sensors of Smartphone
The number of pedestrian accidents continues to keep climbing. Distraction
from smartphone is one of the biggest causes for pedestrian fatalities. In this
paper, we develop SaferCross, a mobile system based on the embedded sensors of
smartphone to improve pedestrian safety by preventing distraction from
smartphone. SaferCross adopts a holistic approach by identifying and developing
essential system components that are missing in existing systems and
integrating the system components into a "fully-functioning" mobile system for
pedestrian safety. Specifically, we create algorithms for improving the
accuracy and energy efficiency of pedestrian positioning, effectiveness of
phone activity detection, and real-time risk assessment. We demonstrate that
SaferCross, through systematic integration of the developed algorithms,
performs situation awareness effectively and provides a timely warning to the
pedestrian based on the information obtained from smartphone sensors and Direct
Wi-Fi-based peer-to-peer communication with approaching cars. Extensive
experiments are conducted in a department parking lot for both component-level
and integrated testing. The results demonstrate that the energy efficiency and
positioning accuracy of SaferCross are improved by 52% and 72% on average
compared with existing solutions with missing support for positioning accuracy
and energy efficiency, and the phone-viewing event detection accuracy is over
90%. The integrated test results show that SaferCross alerts the pedestrian
timely with an average error of 1.6sec in comparison with the ground truth
data, which can be easily compensated by configuring the system to fire an
alert message a couple of seconds early.Comment: Published in IEEE Access, 202
Towards Inferring Mechanical Lock Combinations using Wrist-Wearables as a Side-Channel
Wrist-wearables such as smartwatches and fitness bands are equipped with a
variety of high-precision sensors that support novel contextual and
activity-based applications. The presence of a diverse set of on-board sensors,
however, also expose an additional attack surface which, if not adequately
protected, could be potentially exploited to leak private user information. In
this paper, we investigate the feasibility of a new attack that takes advantage
of a wrist-wearable's motion sensors to infer input on mechanical devices
typically used to secure physical access, for example, combination locks. We
outline an inference framework that attempts to infer a lock's unlock
combination from the wrist motion captured by a smartwatch's gyroscope sensor,
and uses a probabilistic model to produce a ranked list of likely unlock
combinations. We conduct a thorough empirical evaluation of the proposed
framework by employing unlocking-related motion data collected from human
subject participants in a variety of controlled and realistic settings.
Evaluation results from these experiments demonstrate that motion data from
wrist-wearables can be effectively employed as a side-channel to significantly
reduce the unlock combination search-space of commonly found combination locks,
thus compromising the physical security provided by these locks
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