A Continuous Identity Authentication Scheme Based on Physiological and Behavioral Characteristics

Abstract

Wearable devices have flourished over the past ten years providing great advantages to people and, recently, they have also been used for identity authentication. Most of the authentication methods adopt a one-time authentication manner which cannot provide continuous certification. To address this issue, we present a two-step authentication method based on an own-built fingertip sensor device which can capture motion data (e.g., acceleration and angular velocity) and physiological data (e.g., a photoplethysmography (PPG) signal) simultaneously. When the device is worn on the user’s fingertip, it will automatically recognize whether the wearer is a legitimate user or not. More specifically, multisensor data is collected and analyzed to extract representative and intensive features. Then, human activity recognition is applied as the first step to enhance the practicability of the authentication system. After correctly discriminating the motion state, a one-class machine learning algorithm is applied for identity authentication as the second step. When a user wears the device, the authentication process is carried on automatically at set intervals. Analyses were conducted using data from 40 individuals across various operational scenarios. Extensive experiments were executed to examine the effectiveness of the proposed approach, which achieved an average accuracy rate of 98.5% and an F1-score of 86.67%. Our results suggest that the proposed scheme provides a feasible and practical solution for authentication.

A mobile app to make biometrics usage more secure

“…This biometric cryptosystem has 4 of low explosibility and can be used by disabled people or users with some missing physical traits. For Continuous authentication, Guannan et al [25] suggested a two-step authentication method based on motion and physiological movement, i.e., photoplethysmography (PPG) signals, especially for wearable devices. Even continuous authentication protocols can be classified into two categories: a) user-to-device and b) device-to-device [26].…”

Autenticação Contínua e Segura Baseada em Sinais PPG e Comunicação Galvânica

“…Alguns deles consideram características comportamentais ou temporais para a autenticação. Outros seguem estudos práticos utilizando multisensores custosos e requerem constante movimentação do usuário [Wu et al 2018]. [Lin et al 2017] utilizaram a variação de batimentos cardíacos para autenticação contínua, obtendo acurácia de 98,61%.…”

“…A biometria é amplamente estudada na literatura e demonstra um grande potencial para a autenticação de usuários no contexto dos dispositivos vestíveis [Wu et al 2018]. As formas mais comuns de autenticação biométrica passam pelo reconhecimento da impressão digital, da facial ou da íris.…”

Intelligent Authentication for Identity and Access Management: A Review Paper

“…Table1 displays the comparison result and show the ideal authentication framework for IAM will face all challenges. [27] No No No Yes [22] Yes No No Yes [23] Yes Yes Yes No [11] Yes No No Yes [20] No No Yes No [21] Yes Yes No Yes [26] Yes No No Yes [28] No…”