# Machine Learning Algorithms In User Authentication Schemes

arXiv: https://arxiv.org/abs/2110.07826 · HTML: https://mlsanity.com/paper/2110.07826/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection](https://mlsanity.com/paper/2109.05701/) (2021)
- [MultiLock: Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns](https://mlsanity.com/paper/1901.10312/) (2019)

## Built on by (most cited first, 11 of 11)

- [Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning](https://mlsanity.com/paper/2201.08564/) (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)
- [Using Deep Learning to Detecting Deepfakes](https://mlsanity.com/paper/2207.13644/) (2022)
- [A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation](https://mlsanity.com/paper/2201.12705/) (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)
- [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)

## Abstract (arXiv)

In the past two decades, the number of mobile products being created by companies has grown exponentially. However, although these devices are constantly being upgraded with the newest features, the security measures used to protect these devices has stayed relatively the same over the past two decades. The vast difference in growth patterns between devices and their security is opening up the risk for more and more devices to easily become infiltrated by nefarious users. Working off of previous work in the field, this study looks at the different Machine Learning algorithms used in user authentication schemes involving touch dynamics and device movement. This study aims to give a comprehensive overview of the current uses of different machine learning algorithms that are frequently used in user authentication schemas involving touch dynamics and device movement. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper.