Behavioral Biometrics for Continuous Authentication in the Internet-of-Things Era: An Artificial Intelligence Perspective

Liang, Yunji; Samtani, Sagar; Guo, Bin; Yu, Zhiwen

doi:10.1109/jiot.2020.3004077

Cited by 131 publications

(44 citation statements)

References 153 publications

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“…[11]Thus the model finds in challenging to regress or classify BAC using the extracted features. [40] Our approach differs from prior work in the fact that we do not use any manually engineered features. We propose two deep learning algorithms to find the Blood alcohol concentration from just the raw signal without any need for feature extraction and compare the performance of our models with the current feature-based techniques.…”

Section: Related Workmentioning

confidence: 99%

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Estimation of Blood Alcohol Concentration From Smartphone Gait Data Using Neural Networks

Balakrishnan

Nie

et al. 2021

IEEE Access

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Driving is a dynamic activity, which requires quick reflexes and decision making in order to respond to sudden changes in traffic conditions. Alcohol consumption impairs motor and cognitive skills, and causes many driving-related accidents annually. Passive methods of proactively detecting drivers who are too drunk to drive in order to notify them and prevent accidents, have recently been proposed. The effects of alcohol on a drinker's gait (walk) is a reliable indicator of their intoxication level. In this paper, we investigate detecting drinkers' intoxication levels from their gait by using neural networks to analyze sensor data gathered from their smartphone. Using data gathered from a large controlled alcohol study, we perform regression analysis using a Bi-directional Long Short Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) architectures to predict a person's Blood Alcohol Concentration (BAC) from their smartphone's accelerometer and gyroscope data. We innovatively proposed a comprehensive suite of preprocessing techniques and model-specific extensions to vanilla CNN and bi-LSTM models, which are well thought out and adapted specifically for BAC estimation. Our Bi-LSTM architecture achieves an RMSE of 0.0167 and the CNN architecture achieves an RMSE of 0.0168, outperforming state-of-the-art intoxication detection models using Bayesian Regularized Multilayer Perceptrons (MLP) (RMSE of 0.017) and the Random Forest (RF), with hand-crafted features. Moreover, our models learn features from raw sensor data, obviating the need for hand-crafted features, which is time consuming. Moreover, they achieve lower variance across folds and are hence more generalizable.

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Section: Related Workmentioning

confidence: 99%

“…Given below in table 9 are the features used to train the machine learning models,which prior work on smartphone intoxication estimation had found to perform well [7] [11] [40] The list of features contains both walk level and segment level features. A single walk contains a continuous stream of data for a duration of approximately 30 seconds.…”

Section: Appendixmentioning

confidence: 99%

Estimation of Blood Alcohol Concentration From Smartphone Gait Data Using Neural Networks

Balakrishnan

Nie

et al. 2021

IEEE Access

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“…For instance, background noise can affect speechbased authentication, the presence of dirt on fingers can affect fingerprint-based authentication, and poor lighting can affect facial recognition-based authentication. Still, a combined approach can balance the performance loss due to one affected biometric [42]. However, most of these existing multimodal authentications are not adoptable to wearable due to various limitations, as discussed in the previous section (Section I-B1).…”

Section: ) Multi-modal Biometric Authenticationmentioning

confidence: 99%

HIAuth: A Hierarchical Implicit Authentication System for IoT Wearables Using Multiple Biometrics

2021

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Day-by-day wearable devices, such as smartwatches, are getting popular with their wide range of services, including allowing financial transactions, unlocking cars, tracking health and fitness, among many others. Most often, these services are managed based on users' personal data. Unfortunately, due to various limitations, most of the market-wearables either do not have any user authentication or have a knowledge-based authentication, such as passwords, PINs, or pattern locks, which are not only burdensome for users in the age of the internet of things (IoT) when the users are already flooded with so many passwords. Therefore, there is a need for a burden-free implicit authentication mechanism for wearables that can utilize different less-informative soft-biometric data easily obtainable from the market wearables. In this work, we present a hierarchical implicit authentication (HIAuth) system that utilizes the heart rate, gait, and breathing audio signals based on their availability to authenticate a user. From our detailed analysis, we find that binary support vector machine (SVM) classifiers with radial basis function (RBF) kernels can achieve an average accuracy of 0.98±0.04 (non-sedentary) and 0.94±0.03 (sedentary), F 1 score of 0.98±0.03 (non-sedentary) and 0.94±0.04 (sedentary), and genuine rejection rate of about 0.97±0.07 (non-sedentary) and 0.93±0.04 (sedentary), which shows the feasibility and promise of this work.

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“…Preventing malicious operation of SHS devices requires a robust security scheme. In this perspective, three types of conventional security mechanisms have gained a particular attention viz., signature-based intrusion detection systems (IDSs) [6,7], user authentication [8,9], and access control models [10][11][12]. Unfortunately, these approaches suffer from several limitations.…”

Section: Introductionmentioning

confidence: 99%

Anomalous behavior detection-based approach for authenticating smart home system users

Amraoui

Zouari

2021

Int. J. Inf. Secur.

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Behavioral Biometrics for Continuous Authentication in the Internet-of-Things Era: An Artificial Intelligence Perspective

Cited by 131 publications

References 153 publications

Estimation of Blood Alcohol Concentration From Smartphone Gait Data Using Neural Networks

Estimation of Blood Alcohol Concentration From Smartphone Gait Data Using Neural Networks

HIAuth: A Hierarchical Implicit Authentication System for IoT Wearables Using Multiple Biometrics

Anomalous behavior detection-based approach for authenticating smart home system users

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