Conference item icon

Conference item

Deepauth: in-situ authentication for smartwatches via deeply learned behavioural biometrics

Abstract:

This paper proposes DeepAuth, an in-situ authentication framework that leverages the unique motion patterns when users entering passwords as behavioural biometrics. It uses a deep recurrent neural network to capture the subtle motion signatures during password input, and employs a novel loss function to learn deep feature representations that are robust to noise, unseen passwords, and malicious imposters even with limited training data. DeepAuth is by design optimised for resource constrained...

Expand abstract
Publication status:
Published
Peer review status:
Reviewed (other)

Actions


Access Document


Files:
Publisher copy:
10.1145/3267242.3267252

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
Expand authors...
Publisher:
ACM Digital Library Publisher's website
Journal:
Proceedings of the 2018 ACM International Symposium on Wearable Computers Journal website
Pages:
204-207
Host title:
Proceedings of the 2018 ACM International Symposium on Wearable Computers
Publication date:
2018-10-08
DOI:
ISSN:
1550-4816
Source identifiers:
948863
ISBN:
9781450359672
Keywords:
Pubs id:
pubs:948863
UUID:
uuid:439b6b0f-0455-4ae3-b943-5aa0d42e9754
Local pid:
pubs:948863
Deposit date:
2019-08-23

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP