ArticleSensors (Basel, Switzerland)2019
ADLAuth: Passive Authentication Based on Activity of Daily Living Using Heterogeneous Sensing in Smart Cities.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Exploration of Effective Time-Velocity Distribution for Doppler-Radar-Based Personal Gait Identification Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Blockchain-Based Authentication and Trust Management Mechanism for Smart Cities.Sensors (Basel, Switzerland) · 2022Article
- Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor.IEEE journal of translational engineering in health and medicine · 2022Article
- Gait Recognition Using Optical Motion Capture: A Decision Fusion Based Method.Sensors (Basel, Switzerland) · 2021Article
- Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review.Sensors (Basel, Switzerland) · 2020Review
- A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems.Sensors (Basel, Switzerland) · 2020Article
- Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
The Internet of Things is a rapidly growing paradigm for smart cities that provides a way of communication, identification, and sensing capabilities among physically distributed devices. With the evolution of the Internet of Things (IoTs), user dependence on smart systems and services, such as smart appliances, smartphone, security, and healthcare applications, has been increased. This demands secure authentication mechanisms to preserve the users' privacy when interacting with smart devices. This paper proposes a heterogeneous framework "ADLAuth" for passive and implicit authentication of the user using either a smartphone's built-in sensor or wearable sensors by analyzing the physical activity patterns of the users. Multiclass machine learning algorithms are applied to users' identity verification. Analyses are performed on three different datasets of heterogeneous sensors for a diverse number of activities. A series of experiments have been performed to test the effectiveness of the proposed framework. The results demonstrate the better performance of the proposed scheme compared to existing work for user authentication.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.