# Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning

arXiv: https://arxiv.org/abs/2201.08564 · HTML: https://mlsanity.com/paper/2201.08564/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 21 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection](https://mlsanity.com/paper/2109.05701/) (2021)
- [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)
- [MultiLock: Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns](https://mlsanity.com/paper/1901.10312/) (2019)
- [Continuous Authentication Using Mouse Movements, Machine Learning, and Minecraft](https://mlsanity.com/paper/2110.11080/) (2021)
- [Continuous User Authentication via Unlabeled Phone Movement Patterns](https://mlsanity.com/paper/1708.04399/) (2017)
- [Effect of context in swipe gesture-based continuous authentication on smartphones](https://mlsanity.com/paper/1905.11780/) (2019)
- [Swipe dynamics as a means of authentication: results from a Bayesian unsupervised approach](https://mlsanity.com/paper/2008.01013/) (2020)

## Built on by (most cited first, 12 of 12)

- [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)
- [Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM](https://mlsanity.com/paper/2302.10280/) (2023)
- [Leveraging Deep Learning Approaches for Deepfake Detection: A Review](https://mlsanity.com/paper/2304.01908/) (2023)
- [Hybrid Deepfake Detection Utilizing MLP and LSTM](https://mlsanity.com/paper/2304.14504/) (2023)
- [Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamics](https://mlsanity.com/paper/2305.09482/) (2023)
- [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)
- [Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authentication](https://mlsanity.com/paper/2204.09088/) (2022)
- [Mitigating Presentation Attack using DCGAN and Deep CNN](https://mlsanity.com/paper/2207.00161/) (2022)
- [Machine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specifications](https://mlsanity.com/paper/2302.05530/) (2023)
- [Software Repositories and Machine Learning Research in Cyber Security](https://mlsanity.com/paper/2311.00691/) (2023)

## Abstract (arXiv)

In recent years the amount of secure information being stored on mobile devices has grown exponentially. However, current security schemas for mobile devices such as physiological biometrics and passwords are not secure enough to protect this information. Behavioral biometrics have been heavily researched as a possible solution to this security deficiency for mobile devices. This study aims to contribute to this innovative research by evaluating the performance of a multimodal behavioral biometric based user authentication scheme using touch dynamics and phone movement. This study uses a fusion of two popular publicly available datasets the Hand Movement Orientation and Grasp dataset and the BioIdent dataset. This study evaluates our model performance using three common machine learning algorithms which are Random Forest Support Vector Machine and K-Nearest Neighbor reaching accuracy rates as high as 82% with each algorithm performing respectively for all success metrics reported.