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

• URL: http://arxiv.org/abs/2205.13646v1
• Date: Thu, 26 May 2022 21:43:59 GMT
• Title: Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication
• Authors: Nyle Siddiqui, Rushit Dave, Naeem Seliya, Mounika Vanamala
• Abstract summary: This article builds upon our previous published work by evaluating our dataset of 40 users using three machine learning and deep learning algorithms. The top performer is 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%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning [0.0]
  This research aims to further understanding in the field of continuous authentication using behavioral biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing Minecraft with a Samsung Tablet, each for a duration of 15 minutes.
  arXiv  Detail & Related papers  (2024-03-06T16:22:49Z)
• KeyDetect --Detection of anomalies and user based on Keystroke Dynamics [0.0]
  Cyber attacks can easily access sensitive data like credit card details and social security number. Currently to stop cyber attacks, various different methods are opted from using two-step verification methods. We are proposing a technique of using keystroke dynamics (typing pattern) of a user to authenticate the genuine user.
  arXiv  Detail & Related papers  (2023-04-08T09:00:07Z)
• mPSAuth: Privacy-Preserving and Scalable Authentication for Mobile Web Applications [0.0]
  mPSAuth is an approach for continuously tracking various data sources reflecting user behavior and estimating the likelihood of the current user being legitimate. We show that mPSAuth can provide high accuracy with low encryption and communication overhead, while the effort for the inference is increased to a tolerable extent.
  arXiv  Detail & Related papers  (2022-10-07T12:49:34Z)
• First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization [112.40598205054994]
  We formalize this idea as a completely unsupervised objective for optimizing interfaces. We conduct an observational study on 540K examples of users operating various keyboard and eye gaze interfaces for typing, controlling simulated robots, and playing video games. The results show that our mutual information scores are predictive of the ground-truth task completion metrics in a variety of domains.
  arXiv  Detail & Related papers  (2022-05-24T21:57:18Z)
• Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication [6.961253535504979]
  We use a deep convolutional generative adversarial network (DC-GAN) to create adversarial samples. We show that our deep learning model is surprisingly robust to such an attack scenario.
  arXiv  Detail & Related papers  (2021-10-03T00:15:50Z)
• Anomaly Detection in Cybersecurity: Unsupervised, Graph-Based and Supervised Learning Methods in Adversarial Environments [63.942632088208505]
  Inherent to today's operating environment is the practice of adversarial machine learning. In this work, we examine the feasibility of unsupervised learning and graph-based methods for anomaly detection. We incorporate a realistic adversarial training mechanism when training our supervised models to enable strong classification performance in adversarial environments.
  arXiv  Detail & Related papers  (2021-05-14T10:05:10Z)
• Anomaly Detection-Based Unknown Face Presentation Attack Detection [74.4918294453537]
  Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task.
  arXiv  Detail & Related papers  (2020-07-11T21:20:55Z)
• Federated Learning of User Authentication Models [69.93965074814292]
  We propose Federated User Authentication (FedUA), a framework for privacy-preserving training of machine learning models. FedUA adopts federated learning framework to enable a group of users to jointly train a model without sharing the raw inputs. We show our method is privacy-preserving, scalable with number of users, and allows new users to be added to training without changing the output layer.
  arXiv  Detail & Related papers  (2020-07-09T08:04:38Z)
• Detection as Regression: Certified Object Detection by Median Smoothing [50.89591634725045]
  This work is motivated by recent progress on certified classification by randomized smoothing. We obtain the first model-agnostic, training-free, and certified defense for object detection against $ell$-bounded attacks.
  arXiv  Detail & Related papers  (2020-07-07T18:40:19Z)
• Adversarial Self-Supervised Contrastive Learning [62.17538130778111]
  Existing adversarial learning approaches mostly use class labels to generate adversarial samples that lead to incorrect predictions. We propose a novel adversarial attack for unlabeled data, which makes the model confuse the instance-level identities of the perturbed data samples. We present a self-supervised contrastive learning framework to adversarially train a robust neural network without labeled data.
  arXiv  Detail & Related papers  (2020-06-13T08:24:33Z)
• Author2Vec: A Framework for Generating User Embedding [5.805785001237604]
  We propose a novel end-to-end neural network-based user embedding system, Author2Vec. The model incorporates sentence representations generated by BERT with a novel unsupervised pre-training objective, authorship classification. Author2Vec successfully encoded useful user attributes and the generated user embedding performs well in downstream classification tasks.
  arXiv  Detail & Related papers  (2020-03-17T23:31:11Z)

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.