Behavioral Biometrics: Past, Present and Future

Sharma, Mridula; Elmiligi, Haytham

doi:10.5772/intechopen.102841

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…We also stress that Figure 2 represents an abstract view of the system at inference time, while Figure 4 illustrates the AuthentiSense at training time. We utilized online triplet mining [60] to train our network and unfolded the network structure, in online mining, to explain the inner workings of our system.…”

Section: Methodsmentioning

confidence: 99%

“…The approach is to identify unique individual regularities in user's behavior and analyze several parameters such as touch gestures, navigation, and motion patterns, during user's online activities, to detect potential irregularities not related to the real user. Behavioral authentication systems promise to provide increased security due to the dynamic authentication and the resilience to circumvention their traits are hard to emulate or copy [60].…”

Section: Introductionmentioning

confidence: 99%

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AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms

Fereidooni¹,

Koenig²,

Rieger³

et al. 2023

Proceedings 2023 Network and Distributed System Security Symposium

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Mobile applications are widely used for online services sharing a large amount of personal data online. One-time authentication techniques such as passwords and physiological biometrics (e.g., fingerprint, face, and iris) have their own advantages but also disadvantages since they can be stolen or emulated, and do not prevent access to the underlying device, once it is unlocked. To address these challenges, complementary authentication systems based on behavioural biometrics have emerged. The goal is to continuously profile users based on their interaction with the mobile device. However, existing behavioural authentication schemes are not (i) user-agnostic meaning that they cannot dynamically handle changes in the user-base without model re-training, or (ii) do not scale well to authenticate millions of users. In this paper, we present AuthentiSense, a user-agnostic, scalable, and efficient behavioural biometrics authentication system that enables continuous authentication and utilizes only motion patterns (i.e., accelerometer, gyroscope and magnetometer data) while users interact with mobile apps. Our approach requires neither manually engineered features nor a significant amount of data for model training. We leverage a few-shot learning technique, called Siamese network, to authenticate users at a large scale. We perform a systematic measurement study and report the impact of the parameters such as interaction time needed for authentication and n-shot verification (comparison with enrollment samples) at the recognition stage. Remarkably, AuthentiSense achieves high accuracy of up to 97% in terms of F1-score even when evaluated in a few-shot fashion that requires only a few behaviour samples per user (3 shots). Our approach accurately authenticates users only after 1 second of user interaction. For AuthentiSense, we report a FAR and FRR of 0.023 and 0.057, respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms

Fereidooni¹,

Koenig²,

Rieger³

et al. 2023

Proceedings 2023 Network and Distributed System Security Symposium

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Biometric in Cyber Security: A Mini Review

Debas

Alajlan

Rahman

2023

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

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An evaluation of transfer learning models in EEG-based authentication

et al. 2023

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Electroencephalogram(EEG)-based authentication has received increasing attention from researchers as they believe it could serve as an alternative to more conventional personal authentication methods. Unfortunately, EEG signals are non-stationary and could be easily contaminated by noise and artifacts. Therefore, further processing of data analysis is needed to retrieve useful information. Various machine learning approaches have been proposed and implemented in the EEG-based domain, with deep learning being the most current trend. However, retaining the performance of a deep learning model requires substantial computational effort and a vast amount of data, especially when the models go deeper to generate consistent results. Deep learning models trained with small data sets from scratch may experience an overfitting issue. Transfer learning becomes an alternative solution. It is a technique to recognize and apply the knowledge and skills learned from the previous tasks to a new domain with limited training data. This study attempts to explore the applicability of transferring various pre-trained models’ knowledge to the EEG-based authentication domain. A self-collected database that consists of 30 subjects was utilized in the analysis. The database enrolment is divided into two sessions, with each session producing two sets of EEG recording data. The frequency spectrums of the preprocessed EEG signals are extracted and fed into the pre-trained models as the input data. Three experimental tests are carried out and the best performance is reported with accuracy in the range of 99.1–99.9%. The acquired results demonstrate the efficiency of transfer learning in authenticating an individual in this domain.

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Behavioral Biometrics: Past, Present and Future

Cited by 5 publications

References 22 publications

AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms

AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms

Biometric in Cyber Security: A Mini Review

An evaluation of transfer learning models in EEG-based authentication

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