12 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
Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis
Multimodal brain networks characterize complex connectivities among different
brain regions from both structural and functional aspects and provide a new
means for mental disease analysis. Recently, Graph Neural Networks (GNNs) have
become a de facto model for analyzing graph-structured data. However, how to
employ GNNs to extract effective representations from brain networks in
multiple modalities remains rarely explored. Moreover, as brain networks
provide no initial node features, how to design informative node attributes and
leverage edge weights for GNNs to learn is left unsolved. To this end, we
develop a novel multiview GNN for multimodal brain networks. In particular, we
regard each modality as a view for brain networks and employ contrastive
learning for multimodal fusion. Then, we propose a GNN model which takes
advantage of the message passing scheme by propagating messages based on degree
statistics and brain region connectivities. Extensive experiments on two
real-world disease datasets (HIV and Bipolar) demonstrate the effectiveness of
our proposed method over state-of-the-art baselines.Comment: Accepted to ICML 2021 Workshop on Computational Approaches to Mental
Healt
Keystroke Biometrics for Freely Typed Text Based on CNN model
Keystroke biometrics, as an authentication method with advantages of no extra hardware cost, easy-to-integrate and high-security, has attracted much attention in user authentication. However, a mass of researches on keystroke biometrics have focused on the fixed-text analysis, while only a few took free-text analysis into consideration. And in the field of free-text analysis, most researchers usually devote their efforts to extracting the most appropriate keystroke features on their own experience. These methods were inevitably questionable due to their strong subjectivity. In this paper we proposed a multi-user keystroke authentication scheme based on CNN model, which can automatically figure out the appropriate features for the model, adjust and optimize the model constantly to further enhance the performance of model. In the experiment on a small sample set, the performance is improved more than 10% compared with the benchmark. Our model achieves an average recognition accuracy of 92.58%, with FAR of 0.24% and FRR of 7.34%
On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment
In this paper, we study the inference of gender, major/minor (computer
science, non-computer science), typing style, age, and height from the typing
patterns collected from 117 individuals in a multi-device environment. The
inference of the first three identifiers was considered as classification
tasks, while the rest as regression tasks. For classification tasks, we
benchmark the performance of six classical machine learning (ML) and four deep
learning (DL) classifiers. On the other hand, for regression tasks, we
evaluated three ML and four DL-based regressors. The overall experiment
consisted of two text-entry (free and fixed) and four device (Desktop, Tablet,
Phone, and Combined) configurations. The best arrangements achieved accuracies
of 96.15%, 93.02%, and 87.80% for typing style, gender, and major/minor,
respectively, and mean absolute errors of 1.77 years and 2.65 inches for age
and height, respectively. The results are promising considering the variety of
application scenarios that we have listed in this work.Comment: The first two authors contributed equally. The code is available upon
request. Please contact the last autho
Recent Advances in Biometric Technology for Mobile Devices
International audienceThe prevalent commercial deployment of mobile biometrics as a robust authentication method on mobile devices has fueled increasingly scientific attention. Motivated by this, in this work we seek to provide insight on recent development in mobile biometrics. We present parallels and dissimilarities of mobile biometrics and classical biometrics, enumerate related benefits and challenges. Further we provide an overview of recent techniques in mobile biometrics, as well as application systems adopted by industry. Finally, we discuss open research problems in this field