619 research outputs found
Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning
With the rapid growth in smartphone usage, more organizations begin to focus
on providing better services for mobile users. User identification can help
these organizations to identify their customers and then cater services that
have been customized for them. Currently, the use of cookies is the most common
form to identify users. However, cookies are not easily transportable (e.g.,
when a user uses a different login account, cookies do not follow the user).
This limitation motivates the need to use behavior biometric for user
identification. In this paper, we propose DEEPSERVICE, a new technique that can
identify mobile users based on user's keystroke information captured by a
special keyboard or web browser. Our evaluation results indicate that
DEEPSERVICE is highly accurate in identifying mobile users (over 93% accuracy).
The technique is also efficient and only takes less than 1 ms to perform
identification.Comment: 2017 Joint European Conference on Machine Learning and Knowledge
Discovery in Database
Keystroke dynamics as signal for shallow syntactic parsing
Keystroke dynamics have been extensively used in psycholinguistic and writing
research to gain insights into cognitive processing. But do keystroke logs
contain actual signal that can be used to learn better natural language
processing models?
We postulate that keystroke dynamics contain information about syntactic
structure that can inform shallow syntactic parsing. To test this hypothesis,
we explore labels derived from keystroke logs as auxiliary task in a multi-task
bidirectional Long Short-Term Memory (bi-LSTM). Our results show promising
results on two shallow syntactic parsing tasks, chunking and CCG supertagging.
Our model is simple, has the advantage that data can come from distinct
sources, and produces models that are significantly better than models trained
on the text annotations alone.Comment: In COLING 201
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
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