Table 4 Comparison of continuous authentication approaches.

CMBSA

Multimodal (Keystroke + Gait)

WTF + ACF

98.25%

2.35

2.5

2.5

High accuracy and usability, combining keystroke and gait biometrics using a dynamic scoring algorithm.

High

²⁸

Keystroke Dynamics

Transformer-based Neural Network

95.2%

2.12

3.3

2.8

Real-time keystroke authentication using Transformer architecture.

Medium

³⁰

Gait Biometrics (wrist)

LSTM + GRU Hybrid Model

96.8%

3.7

2.5

2.0

Enhances temporal gait feature extraction for continuous authentication.

Medium

³³

Multimodal (Keystroke + Gait)

Deep Fusion Network

97.9%

3.4

1.8

1.9

Gait and keystroke fusion with deep learning reduces spoofing attempts.

High

³⁴

Multimodal (Keystroke + Voice)

CNN + Autoencoder

97.3%

2.9

2.0

1.9

Combines keystroke and voice features, resisting impersonation attacks.

Medium

³¹

Gait Biometrics (smartphone)

Siamese Neural Networks

94.7%

4.1

3.2

2.9

Smartphone-based gait authentication with some sensor inconsistencies.

Low

³⁵

Multimodal (Gait + Face)

CNN + Blockchain

99.8%

1.4

N/A

N/A

Blockchain integration for continuous gait and face authentication security.

High

²⁹

Multimodal (Keystroke + Mouse)

Random Forest + SVM

97.5%

2.8

2.3

2.1

Keystroke and mouse biometrics achieve high accuracy for continuous monitoring.

Medium

³²

Gait Biometrics (wrist)

BiLSTM + Autoencoder

96.4%

3.9

2.7

2.5

Enhances wrist-based gait dynamics for low-resource continuous authentication.

Medium

³⁶

Multimodal (Keystroke + Gait)

Ensemble of CNN + LSTM

98.1%

2.6

2.2

1.9

Combines keystroke and gait biometrics for seamless continuous authentication.

High