A novel identification/verification model using smartphone's sensors and user behavior

Dandachi, Ghina; Hassan, Bachar El; Husseini, Anas El

doi:10.1109/icabme.2013.6648891

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…Therefore, in this paper, we also follow this common trend to devise own method based on SVM for user identification based on gait feature. However, we do not use a single SVM classifier as in existing methods [12,13,15] but take advantages of multiple weak SVM classifiers to boost the overall accuracy.…”

Section: Authentication With Biometric Featuresmentioning

confidence: 99%

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Smart Kiosk with Gait-Based Continuous Authentication

Phan

Dam

Nguyen

et al. 2015

Distributed, Ambient, and Pervasive Interactions

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Abstract. The authors propose to develop a smart kiosk that plays the role of an identity selector activated implicitly when a user is approaching that kiosk. The identity of a user is recognized implicitly in background by a mobile/wearable device based on his or her gait features. Upon arriving at a smart kiosk, the authentication process is performed automatically with the current available user identity in his or her portable device. To realize our system, we propose a new secure authentication scheme compatible with gait-based continuous authentication that can resist against known attacks, including three-factor attacks. Furthermore, we also propose a method to recognize users from their moving patterns using multiple SVM classifiers. Experiments with a dataset with 38 people show that this method can achieve the accuracy up to 92.028 %.

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Section: Authentication With Biometric Featuresmentioning

confidence: 99%

“…Among existing methods for gait recognition, Support Vector Machine is one common approach to classify users from their gait features [12,13,15]. Therefore, in this paper, we also follow this common trend to devise own method based on SVM for user identification based on gait feature.…”

Section: Authentication With Biometric Featuresmentioning

confidence: 99%

Smart Kiosk with Gait-Based Continuous Authentication

Phan

Dam

Nguyen

et al. 2015

Distributed, Ambient, and Pervasive Interactions

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“…Attackers take advantage of and sometimes cause attention failure, using distraction and manipulation to gain access. Thus, it is important to develop the following hypotheses to model unintentional vs intentional clicks or touches on a link on a smartphone to combat this threat because 87% of successful attacks target smartphones, which possess various sensors allowing human behavior modeling [36][37][38][39][40].…”

Section: Hypotheses and Objectivesmentioning

confidence: 99%

Micro-Behavioral Accidental Click Detection System for Preventing Slip-Based Human Error

Almehmadi¹

2021

Sensors

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Accidentally clicking on a link is a type of human error known as a slip in which a user unintentionally performs an unintended task. The risk magnitude is the probability of occurrences of such error with a possible substantial effect to which even experienced individuals are susceptible. Phishing attacks take advantage of slip-based human error by attacking psychological aspects of the users that lead to unintentionally clicking on phishing links. Such actions may lead to installing tracking software, downloading malware or viruses, or stealing private, sensitive information, to list a few. Therefore, a system is needed that detects whether a click on a link is intentional or unintentional and, if unintentional, can then prevent it. This paper proposes a micro-behavioral accidental click detection system (ACDS) to prevent slip-based human error. A within-subject-based experiment was conducted with 20 participants to test the potential of the proposed system. The results reveal the statistical significance between the two cases of intentional vs. unintentional clicks using a smartphone. Random tree, random forest, and support vector machine classifiers were used, exhibiting 82.6%, 87.2%, and 91.6% accuracy in detecting unintentional clicks, respectively.

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“…In short, several studies have experimented user identification using gait recognition as a possible identification method. Our proposed approach utilizes acceleration signals and detects users by their way of walking under different sub-activities in a semi-controlled environment, and for this, a motion-recording device is used in order to measure the acceleration according to the three axes outlined in [14,25].…”

Section: Related Workmentioning

confidence: 99%

“…In the literature hereby referred to, several solutions were proposed addressing implicit user identification without involving the user, such as keystroke-based user identification [54], touch screen biometrics [22,28], application set fingerprints [2], hybrid user identification methods-such as accelerometers and gyroscopes [6,14,52], and gait based user identification [4,43]. However, these solutions only discuss one aspect of user identification, either software or hardware.…”

Section: Introductionmentioning

confidence: 99%

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

Khan

Mazzara

Distefano

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

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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.

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A novel identification/verification model using smartphone's sensors and user behavior

Cited by 12 publications

References 8 publications

Smart Kiosk with Gait-Based Continuous Authentication

Smart Kiosk with Gait-Based Continuous Authentication

Micro-Behavioral Accidental Click Detection System for Preventing Slip-Based Human Error

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

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