A broad review on non-intrusive active user authentication in biometrics

Thomas, Princy Ann; Mathew, K.

doi:10.1007/s12652-021-03301-x

Cited by 15 publications

(7 citation statements)

References 108 publications

(94 reference statements)

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“…A review of non-intrusive active user authentication using biometrics to investigate the merits and demerits of authentication system was investigated in [15]. An overview of numerous continuous authentication systems on mobile devices was analyzed in [16].…”

Section: Continuous User Authentication Progress and Challengesmentioning

confidence: 99%

Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

Jose¹,

Rajasree²

2023

Computer Systems Science and Engineering

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The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like, fingerprint or face. Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users' identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session. However, divergent issues remain unaddressed. This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called, Gaussian Weighted Cauchy Krigingbased Continuous Czekanowski's (GWCK-CC). First, a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise present in the raw input face images. Cauchy Kriging Regression function is employed to reduce the dimensionality. Finally, Continuous Czekanowski's Classification is utilized for proficient classification between the genuine user and attacker. By this way, the proposed GWCK-CC method achieves accurate authentication with minimum error rate and time. Experimental assessment of the proposed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset. The results confirm that the proposed GWCK-CC method enhances authentication accuracy, by 9%, reduces the authentication time, and error rate by 44%, and 43% as compared to the existing methods.

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Section: Continuous User Authentication Progress and Challengesmentioning

confidence: 99%

Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

Jose¹,

Rajasree²

2023

Computer Systems Science and Engineering

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“…It can capture coordinates of a pointing device (such as a mouse) including clientX/Y, pageX/Y, offsetX/Y, and movementX/Y in addition to its events such as click, dblclick, and mousemove without requiring any permission. Beginning as early as 2004 [84], there has been a steady stream of studies demonstrating how mouse movements can be used to identify (or authenticate) users [99]. FP-Radar first detects the abuse of mouserelated keywords for user behavior fingerprinting in 2016 and since then has detected other properties such as movementX/Y, deltaX/Y/Z, and wheelDelta.…”

Section: Analysis Of Apis In the Fingerprinting Clustermentioning

confidence: 99%

“…TouchEvent interface, standardized in 2011 [67] and supported by all major browsers including mobile version of browsers since 2013, 5 is a non-permissioned interface that is similar to mouse interfaces except that it supports simultaneous touches and at different locations on the touch surface. Beginning as early as 2013 [92], there has been a steady stream of studies demonstrating how touch events can be use to identify (or authenticate) users [99]. Specifically, frequency of tapping (captured by events such as ontouchstart, touchenter, touchleave, and touchmove) and strength of tapping (captured by force) can be used for user behavior fingerprinting.…”

Section: Analysis Of Apis In the Fingerprinting Clustermentioning

confidence: 99%

FP-Radar: Longitudinal Measurement and Early Detection of Browser Fingerprinting

Bahrami

Iqbal

Shafiq

2022

Proceedings on Privacy Enhancing Technologies

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Browser fingerprinting is a stateless tracking technique that aims to combine information exposed by multiple different web APIs to create a unique identifier for tracking users across the web. Over the last decade, trackers have abused several existing and newly proposed web APIs to further enhance the browser fingerprint. Existing approaches are limited to detecting a specific fingerprinting technique(s) at a particular point in time. Thus, they are unable to systematically detect novel fingerprinting techniques that abuse different web APIs. In this paper, we propose FP-Radar, a machine learning approach that leverages longitudinal measurements of web API usage on top-100K websites over the last decade for early detection of new and evolving browser fingerprinting techniques. The results show that FP-Radar is able to early detect the abuse of newly introduced properties of already known (e.g., WebGL, Sensor) and as well as previously unknown (e.g., Gamepad, Clipboard) APIs for browser fingerprinting. To the best of our knowledge, FP-Radar is the first to detect the abuse of the Visibility API for ephemeral fingerprinting in the wild.

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“…For example, fingerprint sensors can deceive using fake fingerprints 4 and voice recognition systems can be fooled using voice recordings or mimics. 5 These biometrics can be forged easily and hence are highly susceptible to being misused or failing to provide the level of security the users desire. These forgeries can potentially lead to menacing consequences.…”

Section: Introductionmentioning

confidence: 99%

EEG‐based biometric authentication system using convolutional neural network for military applications

Vadher,

Patel,

Nair

et al. 2023

Security and Privacy

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In this technological era, as the need for security arises, the use of biometrics is increasing in authentication systems as a secure and convenient method of human identification and verification. Electroencephalogram (EEG) signals have gained significant attention among the various biometric modalities available because of their unique and unforgeable characteristics. In this study, we have proposed an EEG‐based multi‐subject and multi‐task biometric authentication system for the military applications that address the challenges associated with multi‐task variation in EEG signals. The proposed work considers the use of respective EEG signals for the access of artillery, entrance to highly confidential places for the military and so forth by authenticated personnel only. We have used a multi‐subject, multi‐session, and multi‐task () dataset. The dataset was partially preprocessed with basic signal processing techniques such as bad channel repairing, independent component analysis for artifact removal, downsampling to 250 Hz, and an audio filter of 0.01–200 Hz for signal improvisation. This partially preprocessed dataset was further processed and was used in our deep learning model (DL) architectures. For EEG‐based biometric authentication, convolutional neural network (CNN) outperforms many of the state‐of‐the‐art DL architectures with a validation accuracy of approximately 99.86%, training accuracy of 98.49% and precision, recall and F1‐score with values of 99.91% that makes this EEG‐based approach for authentication more reliable. The DL models were also compared based on training and inference time, where CNN used the most training time but took the least time to predict the output. We compared the performance of the CNN model for three preprocessing techniques by feeding mel spectrograms, chromagrams and mel frequency cepstral coefficients, out of which mel spectrograms provided better results. This proposed architecture proves to be robust and efficient for military applications.

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A broad review on non-intrusive active user authentication in biometrics

Cited by 15 publications

References 108 publications

Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

FP-Radar: Longitudinal Measurement and Early Detection of Browser Fingerprinting

EEG‐based biometric authentication system using convolutional neural network for military applications

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