Data-Driven Behavioural Biometrics for Continuous and Adaptive User Verification Using Smartphone and Smartwatch

Abstract

Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature. This modality is related to changes in human behaviour over time. The developments in these methods aim to amplify continuous authentication such as biometrics to protect their privacy on user devices. Various Continuous Authentication (CA) systems have been proposed in the literature. They represent a new generation of security mechanisms that continuously monitor user behaviour and use this as the basis to re-authenticate them periodically throughout a login session. However, these methods usually constitute a single classification model which is used to identify or verify a user. This work proposes an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) by introducing a two-step user verification algorithm that verifies the user’s identity using motion-based biometrics and complements the multi-factor authentication, thus making it more secure and flexible. This two-step user verification algorithm is also immune to adversarial attacks, based on our experimental results that show how the rate of misclassification drops while using this model with adversarial data.

A New Post-Processing Proposal for Improving Biometric Gait Recognition Using Wearable Devices

“…From our point of view, the use of non-commercial wearables makes it difficult to extrapolate the results to real applications, so we are interested in works that use smartwatches or smartbands to test our proposal, since these are the ones most closely related to our approach. From a review of the literature, the following works using commercial wearables were found: [ 18 , 19 , 20 , 21 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ].…”

Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

“…The authors discussed the balance between the number of dimensional features and the re-authentication time, which minimizes as the number of dimensions increases. Verma et al [25] proposed an algorithm to balance behavioral biometrics with multi-factor authentication by introducing a two-step user verification algorithm that verifies the user's identity using motion-based biometrics. Table 1 shows recent human activity datasets employed in the studies mentioned above.…”

“…Eight different activities MobiAct [19] 59 Nine different activities WISDM-HARB [21] 51 18 different tasks Hand Movement, Orientation and Grasp (HMOG) [22] 100 Six different activities [25] 5 1 4 3…”

An Artificial Intelligence Approach for Verifying Persons by Employing the Deoxyribonucleic Acid (DNA) Nucleotides

“…Tere are several methods that use biometrics to approve the identity such as fngerprint [1], palm print [2], iris print [3], and voice print [4]. Biometrics include measuring an individual's distinctive physical or behavioral biometric trait [5]. Te Greek word "bio" means life and "metric" means measuring; both words are combined to form the phrase "biometric" [6].…”