Making a good thing better: enhancing password/PIN-based user authentication with smartwatch
Autor: | Wen Tao Zhu, Robert H. Deng, Yingjiu Li, Bing Chang, Qiongxiao Wang |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
lcsh:Computer engineering. Computer hardware
Computer Networks and Communications Computer science Decision tree lcsh:TK7885-7895 02 engineering and technology Login lcsh:QA75.5-76.95 Smartwatch Artificial Intelligence Human–computer interaction Numeric keypad Machine learning 0202 electrical engineering electronic engineering information engineering Wearable technology Sensor Password Authentication business.industry 020206 networking & telecommunications Deep learning User authentication Wearable devices Keystroke dynamics 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science business Software Information Systems |
Zdroj: | Cybersecurity, Vol 1, Iss 1, Pp 1-13 (2018) |
ISSN: | 2523-3246 |
Popis: | Wearing smartwatches becomes increasingly popular in people’s lives. This paper shows that a smartwatch can help its bearer authenticate to a login system effectively and securely even if the bearer’s password has already been revealed. This idea is motivated by our observation that a sensor-rich smartwatch is capable of tracking the wrist motions of its bearer typing a password or PIN, which can be used as an authentication factor. The major challenge in this research is that a sophisticated attacker may imitate a user’s typing behavior as shown in previous research on keystroke dynamics based user authentication. We address this challenge by applying a set of machine learning and deep learning classifiers on the user’s wrist motion data that are collected from a smartwatch worn by the user when inputting his/her password or PIN. Our solution is user-friendly since it does not require users to perform any additional actions when typing passwords or PINs other than wearing smartwatches. We conduct a user study involving 51 participants so as to evaluate the feasibility and performance of our solution. User study results show that the best classifier is the Bagged Decision Trees, which yields 4.58% FRR and 0.12% FAR on a QWERTY keyboard, and 6.13% FRR and 0.16% FAR on a numeric keypad. |
Databáze: | OpenAIRE |
Externí odkaz: |