Izvorni znanstveni članak
https://doi.org/10.32985/ijeces.13.4.6
Bolstering user authentication: a kernel-based fuzzy-clustering model for typing dynamics
Anthony Metumaraibe Nwohiri
orcid.org/0000-0001-7622-7533
; University of Lagos, Faculty of Science, Department of Computer Sciences University Road, Akoka-Yaba, Lagos Nigeria
Ufuoma Cyril Ogude
; University of Lagos, Faculty of Science, Department of Computer Sciences University Road, Akoka-Yaba, Lagos Nigeria
Hai Vinh Nguyen
; Vietnam National University, Faculty of Mathematics, Mechanics and Informatics, Department of Informatics Nguyen Trai Rd, Hanoi, Vietnam
Edilberto Moreno Sanchez
; Autonomous National University of Mexico, Astronomy Institute, Department of Information Technology and Scientific Computing Coyoacán borough, Mexico City, Mexico
Sažetak
In most information systems today, static user authentication is accomplished when the user provides a credential (for example, user ID and the matching password). However, passwords appear to be the most insecure authentication method as they are vulnerable to attacks chiefly caused by poor password hygiene. We contend that an additional, non-intrusive level of security can be achieved by analyzing keystroke biometrics and coming up with a unique biometric template of a user's typing pattern. The paper proposes a new model for representing raw keystroke data collected when analyzing typing biometrics. The model is based on fuzzy sets and kernel functions. The corresponding algorithm is developed. In the static authentication problem, our model demonstrated relatively higher performance than some classic anomaly-detection algorithms, such as Mahalanobis, Manhattan, nearest neighbor, outlier counting, neural network, and the support-vector machine.
Ključne riječi
anomaly detection; data mining; fuzzy clustering; keystroke biometrics; kernel function; machine learning; static authentication
Hrčak ID:
280368
URI
Datum izdavanja:
2.6.2022.
Posjeta: 271 *