Analyzing Key-Click Patterns of PIN Input for Recognizing VoIP Users
| Autor: | Ge Zhang |
|---|---|
| Přispěvatelé: | Karlstad University [Sweden], Jan Camenisch, Simone Fischer-Hübner, Yuko Murayama, Armand Portmann, Carlos Rieder, TC 11 |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Authentication
Voice over IP business.industry Computer science media_common.quotation_subject 020206 networking & telecommunications 02 engineering and technology 16. Peace & justice Speaker recognition Computer security computer.software_genre Identification (information) 0202 electrical engineering electronic engineering information engineering Key (cryptography) Keypad 020201 artificial intelligence & image processing Conversation [INFO]Computer Science [cs] False positive rate business computer media_common |
| Zdroj: | IFIP Advances in Information and Communication Technology 26th International Information Security Conference (SEC) 26th International Information Security Conference (SEC), Jun 2011, Lucerne, Switzerland. pp.247-258, ⟨10.1007/978-3-642-21424-0_20⟩ IFIP Advances in Information and Communication Technology ISBN: 9783642214233 SEC |
| DOI: | 10.1007/978-3-642-21424-0_20⟩ |
| Popis: | Part 7: Privacy Attacks and Privacy-Enhancing Technologies; International audience; Malicious intermediaries are able to detect the availability of VoIP conversation flows in a network and observe the IP addresses used by the conversation partners. However, it is insufficient to infer the calling records of a particular user in this way since the linkability between a user and a IP address is uncertain: users may regularly change or share IP addresses. Unfortunately, VoIP flows may contain humanspecific features. For example, users sometimes are required to provide Personal identification numbers (PINs) to a voice server for authentication and thus the key-click patterns of entering a PIN can be extracted from VoIP flows for user recognition. We invited 31 subjects to enter 4-digital PINs on a virtual keypad of a popular VoIP user-agent with mouse clicking. Employing machine learning algorithms, we achieved average equal error rates of 10-29% for user verification and a hitting rate up to 65% with a false positive rate around 1% for user classification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|