# Data-driven behavioural biometrics for continuous and adaptive user verification using Smartphone and Smartwatch

arXiv: https://arxiv.org/abs/2110.03149 · HTML: https://mlsanity.com/paper/2110.03149/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Authentication of Smartphone Users Using Behavioral Biometrics](https://mlsanity.com/paper/1911.04104/) (2019)
- [Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics](https://mlsanity.com/paper/1905.11831/) (2019)
- [Adversarial Attacks on Deep Learning Systems for User Identification based on Motion Sensors](https://mlsanity.com/paper/2009.01109/) (2020)
- [IoT Security and Authentication schemes Based on Machine Learning: Review](https://mlsanity.com/paper/2109.02695/) (2021)

## Built on by (most cited first, 2 of 2)

- [Deep Learning-Based Multi-Factor Authentication: A Survey of Biometric and Smart Card Integration Approaches](https://mlsanity.com/paper/2510.05163/) (2025)
- [Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authentication](https://mlsanity.com/paper/2204.09088/) (2022)

## Abstract (arXiv)

Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature. This modality is related to change in human behaviour over time. The developments in these methods aim to amplify continuous authentication such as biometrics to protect their privacy on user devices. Various Continuous Authentication (CA) systems have been proposed in the literature. They represent a new generation of security mechanisms that continuously monitor user behaviour and use this as the basis to re-authenticate them periodically throughout a login session. However, these methods usually constitute a single classification model which is used to identify or verify a user. This work proposes an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) by introducing a two-step user verification algorithm that verifies the user's identity using motion-based biometrics and complements the multi-factor authentication, thus making it more secure and flexible. This two-step user verification algorithm is also immune to adversarial attacks, based on our experimental results which show how the rate of misclassification drops while using this model with adversarial data.