Analysis and Evaluation of Keystroke Dynamics as a Feature of Contextual Authentication


Bicakci K., Salman O., Uzunay Y., Tan M.

International Conference on Information Security and Cryptology (ISCTURKEY), ELECTR NETWORK, 3 - 04 Aralık 2020, ss.11-17 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/iscturkey51113.2020.9307967
  • Basıldığı Ülke: ELECTR NETWORK
  • Sayfa Sayıları: ss.11-17
  • Anahtar Kelimeler: User Authentication, Keystroke Dynamics, Contextual Authentication, Behavioural Biometrics, Machine Learning, Anomaly Detection
  • İstanbul Teknik Üniversitesi Adresli: Hayır

Özet

The current best practice dictates that even when the correct username and password are entered, the system should look for login anomalies that might indicate malicious attempts. Most anomaly detection approaches examine static properties of user's contextual data such as II' address, screen size and browser type. Keystroke Dynamics bring additional security measure and enable us to use individuals' keystroke behaviour to decide legitimacy of the user. In this paper. we first analyze different anomaly detection approaches separately and then show accuracy improvements when we combine these solutions with various methods. Our results show that including keystroke dynamics scores in session context anomaly component as a new feature performs better than ensemble methods with different weights for session context and keystroke dynamics components. We argue that this is due to the opportunity to capture the behavioral deviations of the individuals in our augmented model.