# Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamics

arXiv: https://arxiv.org/abs/2305.09482 · HTML: https://mlsanity.com/paper/2305.09482/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 24 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection](https://mlsanity.com/paper/2109.05701/) (2021)
- [Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning](https://mlsanity.com/paper/2201.08564/) (2022)
- [Machine Learning Algorithms In User Authentication Schemes](https://mlsanity.com/paper/2110.07826/) (2021)
- [A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods](https://mlsanity.com/paper/2110.07832/) (2021)
- [Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication](https://mlsanity.com/paper/2205.13646/) (2022)
- [Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning](https://mlsanity.com/paper/2201.08565/) (2022)
- [A Close Look into Human Activity Recognition Models using Deep Learning](https://mlsanity.com/paper/2204.13589/) (2022)
- [Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning](https://mlsanity.com/paper/2205.08371/) (2022)
- [Using Deep Learning to Detecting Deepfakes](https://mlsanity.com/paper/2207.13644/) (2022)
- [Continuous User Authentication Using Machine Learning and Multi-Finger Mobile Touch Dynamics with a Novel Dataset](https://mlsanity.com/paper/2207.13648/) (2022)
- [Techniques for Continuous Touch-Based Authentication Modeling](https://mlsanity.com/paper/2207.12140/) (2022)

## Built on by (most cited first, 1 of 1)

- [Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning](https://mlsanity.com/paper/2403.03832/) (2024)

## Abstract (arXiv)

The aim of this research paper is to look into the use of continuous authentication with mobile touch dynamics, using three different algorithms: Neural Network, Extreme Gradient Boosting, and Support Vector Machine. Mobile devices are constantly increasing in popularity in the world, today smartphone subscriptions have surpassed 6 billion. Mobile touch dynamics refer to the distinct patterns of how a user interacts with their mobile device, this includes factors such as touch pressure, swipe speed, and touch duration. Continuous authentication refers to the process of continuously verifying a user's identity while they are using a device, rather than just at the initial login. This research used a dataset of touch dynamics collected from 40 subjects using the LG V30+. The participants played four mobile games, PUBG, Diep.io, Slither, and Minecraft, for 10 minutes each game. The three algorithms were trained and tested on the extracted dataset, and their performance was evaluated based on metrics such as accuracy, precision, false negative rate, and false positive rate. The results of the research showed that all three algorithms were able to effectively classify users based on their individual touch dynamics, with accuracy ranging from 80% to 95%. The Neural Network algorithm performed the best, achieving the highest accuracy and precision scores, followed closely by XGBoost and SVC. The data shows that continuous authentication using mobile touch dynamics has the potential to be a useful method for enhancing security and reducing the risk of unauthorized access to personal devices. This research also notes the importance of choosing the correct algorithm for a given dataset and use case, as different algorithms may have varying levels of performance depending on the specific task.