# Sensor-based Continuous Authentication of Smartphones' Users Using Behavioral Biometrics: A Contemporary Survey

arXiv: https://arxiv.org/abs/2001.08578 · HTML: https://mlsanity.com/paper/2001.08578/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

## Built on by (most cited first, 10 of 10)

- [By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Prompting](https://mlsanity.com/paper/2407.10385/) (2024)
- [Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions](https://mlsanity.com/paper/2201.00768/) (2022)
- [TypeFormer: Transformers for Mobile Keystroke Biometrics](https://mlsanity.com/paper/2212.13075/) (2022)
- [Mobile Keystroke Biometrics Using Transformers](https://mlsanity.com/paper/2207.07596/) (2022)
- [HLL: Can Agents Cross Humanity's Last Line of Verification?](https://mlsanity.com/paper/2606.02449/) (2026)
- [Examining Uniqueness and Permanence of the WAY EEG GAL dataset toward User Authentication](https://mlsanity.com/paper/2209.04802/) (2022)
- [Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices](https://mlsanity.com/paper/2210.12964/) (2022)
- [Should they? Mobile Biometrics and Technopolicy meet Queer Community Considerations](https://mlsanity.com/paper/2311.10725/) (2023)
- [Scalable Secure Biometric Authentication without Auxiliary Identifiers](https://mlsanity.com/paper/2604.25071/) (2026)
- [Continuous Behavioral Authentication via Multi-Expert BERT Log Analysis for Secure Data Sharing](https://mlsanity.com/paper/2606.21900/) (2026)

## Abstract (arXiv)

Mobile devices and technologies have become increasingly popular, offering comparable storage and computational capabilities to desktop computers allowing users to store and interact with sensitive and private information. The security and protection of such personal information are becoming more and more important since mobile devices are vulnerable to unauthorized access or theft. User authentication is a task of paramount importance that grants access to legitimate users at the point-of-entry and continuously through the usage session. This task is made possible with today's smartphones' embedded sensors that enable continuous and implicit user authentication by capturing behavioral biometrics and traits. In this paper, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods (28 studies), gait-based methods (19 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods (34 studies). The survey provides an overview of the current state-of-the-art approaches for continuous user authentication using behavioral biometrics captured by smartphones' embedded sensors, including insights and open challenges for adoption, usability, and performance.