BodyLock: Human Identity Recogniser App from Walking Activity Data

Kašys, Karolis; Dundulis, Aurimas; Vasiljevas, Mindaugas; Maskeliūnas, Rytis; Damaševičius, Robertas

doi:10.1007/978-3-030-58802-1_23

Cited by 3 publications

(1 citation statement)

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“…Damaševicius et al [ 25 ] used random projections to reduce feature dimensionality to two, followed by computing the Jaccard distance between two probability distributed functions of the derived features for positive identification. Kašys et al [ 26 ] performed user identity verification using linear Support Vector Machine (SVM) classifier on his/her walking activity data captured by the mobile phone. Xu et al [ 27 ] presented Gait-watch, a context-aware authentication system based on gait feature recognition and extraction under various walking activities.…”

Section: Related Workmentioning

confidence: 99%

Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices

Zeng

Zhang

Yang

et al. 2021

Sensors

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Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

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Section: Related Workmentioning

confidence: 99%