Extended sammon projection and wavelet kernel extreme learning machine for gait-based legitimate user identification

Abstract

Smartphones have ubiquitously integrated into our home and work environments, however, the user normally relies on explicit but inefficient identification processes in a controlled environment. Therefore, when the device is stolen, the attacker can have access to the user's personal information and services against the stored password/s. As a result of this potential scenario, this work demonstrates the possibilities of legitimate user identification in a semicontrolled environment through the built-in smartphones motion dynamics captured by two different sensors. This is a two-fold process, sub-activity recognition based user identification using the wavelet kernel extreme learning machine (KELM) to first recognize the performed activity and then identifies whether the recognized activity is performed by the legitimate user or impostor. Prior to the identification; Extended Sammon Projection (ESP) method has been deployed to reduce the redundancy between the features. To validate the proposed system, we first collected data from 20 users walking with their device freely placed in one of their pants pockets (front right, front left, back right and back left pocket). Through extensive experimentation's using overall and averageone-subject-cross-validation, we demonstrate that together time and frequency domain features optimized by ESP to train the KELM is an effective system to identify the legitimate user or an impostor with 97 − 98% overall accuracy.

Smartwatch-Based Legitimate User Identification for Cloud-Based Secure Services

“…ese sensors support similar capabilities and applications of smartphones such as healthcare applications that require physical activity recognition (PAR). e accelerometer, linear accelerometer, magnetometer, and gyroscope sensors are ideal for PAR and gaitbased legitimate user identification over SPs [4][5][6][7]. is work will show that SWs are equally capable of performing PAR and gait-based legitimate user identification.…”

“…Besides, these several traditional legitimate user identification approaches have been proposed based on passwords such as secret information possession and physiological biometrics such as iris patterns and fingerprints. More recently, behavior-based legitimate user identification utilizes the distinct behavior of users such as gestures and gaits [5,7].…”

“…In contrast to above-discussed solutions, behavior-based legitimate user identification assumes that the people have distinct but stable patterns for a certain behavior such as gait [5,7,12], handwriting patterns [13,14], and GPS patterns [15]. Such legitimate user identification exploits users' behavioral patterns to identify a legitimate user.…”

“…is technique permits the use of traditional machine learning classification approaches to handling the time series data. Our proposed study also utilizes the same sliding window approach employed in the prior work [2,[5][6][7]. e discussed windowing process initially partitions the time series raw signal into 30 seconds non-overlapping windows.…”

Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem

“…ARIMA model is a widely used timeseries forecasting model introduced by Box and Jenkins in 1970 [12]. ARIMA model is a general linear stochastic model which is the combination of autoregressive and movingaverage models [13][14][15]. An autoregressive model uses a linear combination of past values to predict the variable of interest.…”