# Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication

arXiv: https://arxiv.org/abs/2205.13646 · HTML: https://mlsanity.com/paper/2205.13646/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 3 of 3)

- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics](https://mlsanity.com/paper/1905.11831/) (2019)
- [Intrusion Detection Using Mouse Dynamics](https://mlsanity.com/paper/1810.04668/) (2018)

## Built on by (most cited first, 9 of 9)

- [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)
- [Machine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specifications](https://mlsanity.com/paper/2302.05530/) (2023)
- [Recent Advancements In The Field Of Deepfake Detection](https://mlsanity.com/paper/2308.05563/) (2023)
- [Software Repositories and Machine Learning Research in Cyber Security](https://mlsanity.com/paper/2311.00691/) (2023)

## Abstract (arXiv)

Static authentication methods, like passwords, grow increasingly weak with advancements in technology and attack strategies. Continuous authentication has been proposed as a solution, in which users who have gained access to an account are still monitored in order to continuously verify that the user is not an imposter who had access to the user credentials. Mouse dynamics is the behavior of a users mouse movements and is a biometric that has shown great promise for continuous authentication schemes. This article builds upon our previous published work by evaluating our dataset of 40 users using three machine learning and deep learning algorithms. Two evaluation scenarios are considered: binary classifiers are used for user authentication, with the top performer being a 1-dimensional convolutional neural network with a peak average test accuracy of 85.73% across the top 10 users. Multi class classification is also examined using an artificial neural network which reaches an astounding peak accuracy of 92.48% the highest accuracy we have seen for any classifier on this dataset.