Context-Dependent Implicit Authentication for Wearable Device User

• URL: http://arxiv.org/abs/2008.12145v1
• Date: Tue, 25 Aug 2020 04:34:19 GMT
• Title: Context-Dependent Implicit Authentication for Wearable Device User
• Authors: William Cheung and Sudip Vhaduri
• Abstract summary: We present a context-dependent soft-biometric-based wearable authentication system utilizing the heart rate, gait, and breathing audio signals. From our detailed analysis, we find that a binary support vector machine (SVM) with radial basis function (RBF) kernel can achieve an average accuracy of 92.84%.
• Score: 1.827510863075184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As market wearables are becoming popular with a range of services, including making financial transactions, accessing cars, etc. that they provide based on various private information of a user, security of this information is becoming very important. However, users are often flooded with PINs and passwords in this internet of things (IoT) world. Additionally, hard-biometric, such as facial or finger recognition, based authentications are not adaptable for market wearables due to their limited sensing and computation capabilities. Therefore, it is a time demand to develop a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from the market wearables. In this work, we present a context-dependent soft-biometric-based wearable authentication system utilizing the heart rate, gait, and breathing audio signals. From our detailed analysis, we find that a binary support vector machine (SVM) with radial basis function (RBF) kernel can achieve an average accuracy of $0.94 \pm 0.07$, $F_1$ score of $0.93 \pm 0.08$, an equal error rate (EER) of about $0.06$ at a lower confidence threshold of 0.52, which shows the promise of this work.

Related papers

• Beyond Gaze Points: Augmenting Eye Movement with Brainwave Data for Multimodal User Authentication in Extended Reality [4.114205202954365]
  We introduce a multimodal biometric authentication system that combines eye movement and brainwave patterns. Our system yields an excellent Equal Error Rate (EER) of 0.298%, which means an 83.6% reduction in EER compared to the single eye movement modality.
  arXiv  Detail & Related papers  (2024-04-29T13:42:55Z)
• Leveraging Machine Learning for Wi-Fi-based Environmental Continuous Two-Factor Authentication [0.44998333629984877]
  We present a novel 2FA approach replacing the user's input with decisions made by Machine Learning (ML) Our system exploits unique environmental features associated with the user, such as beacon frame characteristics and Received Signal Strength Indicator ( RSSI) values from Wi-Fi Access Points (APs) For enhanced security, our system mandates that the user's two devices (i.e., a login device and a mobile device) be situated within a predetermined proximity before granting access.
  arXiv  Detail & Related papers  (2024-01-12T14:58:15Z)
• SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning [55.84746218227712]
  We develop SqueezerFaceNet, a light face recognition network which less than 1M parameters. We show that it can be further reduced (up to 40%) without an appreciable loss in performance.
  arXiv  Detail & Related papers  (2023-07-20T08:38:50Z)
• 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)
• Locally Authenticated Privacy-preserving Voice Input [10.82818142802482]
  Service providers must authenticate their users, although individuals may wish to maintain privacy. Preserving privacy while performing authentication is challenging, particularly where adversaries can use biometric data to train transformation tools. We introduce a secure, flexible privacy-preserving system to capture and store an on-device fingerprint of the users' raw signals.
  arXiv  Detail & Related papers  (2022-05-27T14:56:01Z)
• 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)
• 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)
• Opportunistic Implicit User Authentication for Health-Tracking IoT Wearables [1.8352113484137629]
  We explore the usefulness of blood oxygen saturation SpO2 values collected from the Oximeter device to distinguish a user from others. From a cohort of 25 subjects, we find that 92% of the cases SpO2 can distinguish pairs of users. These results show promise in using SpO2 along with other biometrics to develop implicit continuous authentications for wearables.
  arXiv  Detail & Related papers  (2021-09-28T13:18:36Z)
• ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning [80.85273827468063]
  Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable. We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts. We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
  arXiv  Detail & Related papers  (2021-03-23T15:04:44Z)
• Continuous Authentication of Wearable Device Users from Heart Rate, Gait, and Breathing Data [1.827510863075184]
  Security of private information is becoming the bedrock of an increasingly digitized society. Recent biometric-based authentication methods, such as facial or finger recognition, are getting popular due to their higher accuracy. We present a context-dependent soft-biometric-based authentication system for wearables devices using heart rate, gait, and breathing audio signals.
  arXiv  Detail & Related papers  (2020-08-25T01:55:07Z)
• Mind the GAP: Security & Privacy Risks of Contact Tracing Apps [75.7995398006171]
  Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy. We demonstrate that in real-world scenarios the GAP design is vulnerable to (i) profiling and possibly de-anonymizing persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts.
  arXiv  Detail & Related papers  (2020-06-10T16:05:05Z)

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.