Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning

• URL: http://arxiv.org/abs/2205.08371v1
• Date: Sat, 7 May 2022 05:16:34 GMT
• Title: Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning
• Authors: Laura Pryor, Jacob Mallet, Rushit Dave, Naeem Seliya, Mounika Vanamala, Evelyn Sowells Boone
• Abstract summary: This study contributes to the research being done on behavioral biometrics by creating and evaluating a user authentication scheme using behavioral biometrics. The behavioral biometrics used in this study include touch dynamics and phone movement. We evaluate the performance of different single-modal and multi-modal combinations of the two biometrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The amount of secure data being stored on mobile devices has grown immensely in recent years. However, the security measures protecting this data have stayed static, with few improvements being done to the vulnerabilities of current authentication methods such as physiological biometrics or passwords. Instead of these methods, behavioral biometrics has recently been researched as a solution to these vulnerable authentication methods. In this study, we aim to contribute to the research being done on behavioral biometrics by creating and evaluating a user authentication scheme using behavioral biometrics. The behavioral biometrics used in this study include touch dynamics and phone movement, and we evaluate the performance of different single-modal and multi-modal combinations of the two biometrics. Using two publicly available datasets - BioIdent and Hand Movement Orientation and Grasp (H-MOG), this study uses seven common machine learning algorithms to evaluate performance. The algorithms used in the evaluation include Random Forest, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Logistic Regression, Multilayer Perceptron, and Long Short-Term Memory Recurrent Neural Networks, with accuracy rates reaching as high as 86%.

Related papers

• Biometrics Employing Neural Network [0.0]
  Fingerprints, iris and retina patterns, facial recognition, hand shapes, palm prints, and voice recognition are frequently used forms of biometrics. For systems to be effective and widely accepted, the error rate in recognition and verification must approach zero. Artificial Neural Networks, which simulate the human brain's operations, present themselves as a promising approach.
  arXiv  Detail & Related papers  (2024-02-01T03:59:04Z)
• Privacy-preserving Multi-biometric Indexing based on Frequent Binary Patterns [7.092869001331781]
  We propose an efficient privacy-preserving multi-biometric identification system that retrieves protected deep cancelable templates. A multi-biometric binning scheme is designed to exploit the low intra-class variation properties contained in the frequent binary patterns extracted from different types of biometric characteristics.
  arXiv  Detail & Related papers  (2023-10-04T18:18:24Z)
• Multi-Channel Time-Series Person and Soft-Biometric Identification [65.83256210066787]
  This work investigates person and soft-biometrics identification from recordings of humans performing different activities using deep architectures. We evaluate the method on four datasets of multi-channel time-series human activity recognition (HAR) Soft-biometric based attribute representation shows promising results and emphasis the necessity of larger datasets.
  arXiv  Detail & Related papers  (2023-04-04T07:24:51Z)
• Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification [67.64124512185087]
  Soft biometrics such as gait are widely used with face in surveillance tasks like person recognition and re-identification. We propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks.
  arXiv  Detail & Related papers  (2023-03-24T05:28:35Z)
• Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs [61.080598804629375]
  People with chronic pain unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities. We proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.
  arXiv  Detail & Related papers  (2022-12-20T12:56:28Z)
• Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation [63.05890836038913]
  We study the role of soft biometrics to enhance person recognition systems in unconstrained scenarios. We consider two assumptions: 1) manual estimation of soft biometrics and 2) automatic estimation from two commercial off-the-shelf systems. Experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning.
  arXiv  Detail & Related papers  (2022-10-24T11:29:57Z)
• Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices [0.9005431161010408]
  Behaviour biometrics are being explored as a viable alternative to overcome the limitations of traditional authentication methods. Recent behavioural biometric solutions use deep learning models that require large amounts of annotated training data. We propose using SimSiam-based non-contrastive self-supervised learning to improve the label efficiency of behavioural biometric systems.
  arXiv  Detail & Related papers  (2022-10-24T05:56:32Z)
• Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
  This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits. Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
  arXiv  Detail & Related papers  (2022-03-14T17:05:59Z)
• Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning [0.0]
  This study aims to contribute to this innovative research by evaluating the performance of a multimodal behavioral biometric based user authentication scheme. 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%.
  arXiv  Detail & Related papers  (2022-01-21T06:51:46Z)
• Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms [58.156733807470395]
  This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign. The campaign targeted the application of physical access control in a medium-size establishment with some 500 persons. To the best of our knowledge, this is the first attempt to benchmark quality-based multimodal fusion algorithms.
  arXiv  Detail & Related papers  (2021-11-17T13:39:48Z)
• Data-driven behavioural biometrics for continuous and adaptive user verification using Smartphone and Smartwatch [0.0]
  We propose an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) This work proposes a two-step user verification algorithm that verifies the user's identity using motion-based biometrics.
  arXiv  Detail & Related papers  (2021-10-07T02:46:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.