# Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

arXiv: https://arxiv.org/abs/1708.09754 · HTML: https://mlsanity.com/paper/1708.09754/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2017.

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

- [BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation](https://mlsanity.com/paper/2206.02502/) (2022)
- [Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication](https://mlsanity.com/paper/2211.05662/) (2022)
- [iCTGAN--An Attack Mitigation Technique for Random-vector Attack on Accelerometer-based Gait Authentication Systems](https://mlsanity.com/paper/2210.00615/) (2022)
- [GANTouch: An Attack-Resilient Framework for Touch-based Continuous Authentication System](https://mlsanity.com/paper/2210.01594/) (2022)
- [CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication](https://mlsanity.com/paper/2405.07174/) (2024)
- [Smartphone Impostor Detection with Behavioral Data Privacy and Minimalist Hardware Support](https://mlsanity.com/paper/2103.06453/) (2021)
- [Risk-Aware Fine-Grained Access Control in Cyber-Physical Contexts](https://mlsanity.com/paper/2108.12739/) (2021)
- [AI-Assisted Authentication: State of the Art, Taxonomy and Future Roadmap](https://mlsanity.com/paper/2204.12492/) (2022)
- [Dictionary Attack on IMU-based Gait Authentication](https://mlsanity.com/paper/2309.11766/) (2023)
- [Beyond Gait: Person Identification from Millimeter-Wave Point Clouds Across Activities of Daily Living](https://mlsanity.com/paper/2609.08818/) (2026)

## Abstract (arXiv)

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.