🤖 AI Summary
Existing classification approaches for authentication technologies are often confined to a single dimension, rendering them inadequate for capturing the diversity of authenticators and mechanisms. This work proposes the first multidimensional and extensible classification framework for authentication technologies and authenticators, leveraging large language models to assist in literature screening, semantic clustering, and systematic modeling. Drawing on a corpus of 345 scholarly publications, the study constructs a structured catalog of authenticators and techniques. By transcending the limitations of conventional taxonomies, this framework establishes a unified and flexible reference system for the authentication domain, offering a more comprehensive and adaptable foundation for future research and standardization efforts.
📝 Abstract
Authentication is a fundamental security means for protecting system resources. Authenticator-centric authentication techniques (AuthN Techniques) address how mechanisms and credentials are used via Authenticators. There are many AuthN Techniques that differ in many ways and there exist classification approaches that aim to structure them. However, they are limited in the aspects they classify and are not flexible enough to accommodate the diverse nature of AuthN Techniques. This paper presents two contributions. First, novel, faceted classification schemes for AuthN Techniques and Authenticators are presented. The schemes were developed based on 345 papers identified through a targeted LLM-assisted literature review and semantic clustering. The classification schemes were applied to build a catalog of Authenticators and AuthN Techniques; the second contribution of this paper. This paper presents our methodology, the classification schemes with example applications, the list of AuthN Techniques from the catalog, and discussions on future work.