mlsanityData to 14 Oct 2026

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

arXiv 2201.08564 ↗First posted 21 Jan 2022cs.CR cs.LGcited by 12

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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.

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

8 of 27 references.

References · 8

  1. 2021Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detectioncited by 15
  2. 2021Machine Learning Algorithms In User Authentication Schemescited by 11
  3. 2021A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methodscited by 10
  4. 2019MultiLock: Mobile Active Authentication based on Multiple Biometric and Behavioral Patternscited by 8
  5. 2021Continuous Authentication Using Mouse Movements, Machine Learning, and Minecraftcited by 4
  6. 2017Continuous User Authentication via Unlabeled Phone Movement Patternscited by 2
  7. 2019Effect of context in swipe gesture-based continuous authentication on smartphonescited by 1
  8. 2020Swipe dynamics as a means of authentication: results from a Bayesian unsupervised approachcited by 1

Built on by

Cited by year

Citing papers per year: 2022: 5; 2023: 6; 2024: 1.

12 papers.

  1. 2022A Close Look into Human Activity Recognition Models using Deep Learningcited by 8
  2. 2022Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learningcited by 6
  3. 2022Using Deep Learning to Detecting Deepfakescited by 6
  4. 2023Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVMcited by 3
  5. 2023Leveraging Deep Learning Approaches for Deepfake Detection: A Reviewcited by 2
  6. 2023Hybrid Deepfake Detection Utilizing MLP and LSTMcited by 1
  7. 2023Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamicscited by 1
  8. 2024Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learningcited by 1
  9. 2022Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authenticationcited by 0
  10. 2022Mitigating Presentation Attack using DCGAN and Deep CNNcited by 0
  11. 2023Machine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specificationscited by 0
  12. 2023Software Repositories and Machine Learning Research in Cyber Securitycited by 0