A mobile platform for sociability-based continuous identification
| Autor: | Melike Erol-Kantarci, Burak Kantarci, Stephanie Schuckers, Fazel Anjomshoa |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
020203 distributed computing
Authentication Biometrics Computer science business.industry Fingerprint (computing) Mobile computing 020206 networking & telecommunications 02 engineering and technology Session (web analytics) World Wide Web Identification (information) Human–computer interaction Server 0202 electrical engineering electronic engineering information engineering Mobile telephony business |
| Zdroj: | CAMAD |
| DOI: | 10.1109/camad.2016.7790347 |
| Popis: | By exploiting the ever-expanding amount of data on smart devices and transforming it to meaningful information, it may be possible to uniquely identify a person via user behavior while interacting with their own smart devices. In previous works, identification of users via biometric properties such as fingerprint, iris has been successfully employed to increase the security of access. However, with the popularity of social networks, identification based on behavior over social networks is emerging as a novel concept. In this paper, We use real traces of data collected over several months. The feature set used in the paper includes location of users, their data usage, number of sessions and session duration for Facebook, Linkedin, WhatsApp, Skype and Twitter applications. The collected feature set is aggregated over time and analyzed using machine learning techniques. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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