Conference item
Deepauth: in-situ authentication for smartwatches via deeply learned behavioural biometrics
- Abstract:
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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...
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- Publication status:
- Published
- Peer review status:
- Reviewed (other)
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- Files:
-
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(Accepted manuscript, pdf, 426.2KB)
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- Publisher copy:
- 10.1145/3267242.3267252
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Authors
Bibliographic Details
- 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:
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1550-4816
- Source identifiers:
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948863
- ISBN:
- 9781450359672
Item Description
- Keywords:
- Pubs id:
-
pubs:948863
- UUID:
-
uuid:439b6b0f-0455-4ae3-b943-5aa0d42e9754
- Local pid:
- pubs:948863
- Deposit date:
- 2019-08-23
Terms of use
- Copyright holder:
- ACM
- Copyright date:
- 2018
- Notes:
- © ACM 2019. This paper was presented at the International Symposium on Wearable Computers (IWSC) 2018, in Singapore, 8-12 October 2018. This is the accepted manuscript version of the article. The final version of the paper can be found on ACM Digital Library at: https://doi.org/10.1145/3267242.3267252
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