Keystroke Dynamics Advances for Mobile Devices Using Deep Neural Network

Deng, Yunbin; Zhong, Yu

doi:10.15579/gcsr.vol2.ch4

Cited by 16 publications

(19 citation statements)

References 26 publications

(28 reference statements)

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“…Similarly, it explores gated recurrent unit and bidirectional recurrent neural network (GRU-BRNN) to build the identification model while our work explored the dense neural network. Also, [39] the deep neural network is explored to develop an authentication scheme based on user keystroke dynamics on mobile phone. Furthermore, our work explored supervise learning dense neural network on 71 different features of keystroke dynamics to build an authentication model while [39] applied deep neural network unsupervised learning on timing, tapping and inertial attributes of keystroke dynamics to develop an authentication scheme.…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

Dense Deep Neural Network Architecture for Keystroke Dynamics Authentication in Mobile Phone

Gabralla¹

2020

Adv. sci. technol. eng. syst. j.

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The ever-growing technology in mobile smartphones has enabled users to store sensitive and private information; as a result, it required the need for an improved security system. Previous approaches heavily relied on shallow machine learning algorithms that require feature extraction which is time consuming, laborious and can cause, resulting in poor authentication. In this paper, we propose a deep learning-dense neural network to avoid the limitation of the classical algorithms and build a mobile smartphone touch screen authentication scheme based on keystroke dynamics. A deep learningdense neural network classifier was trained using keystroke dynamics features extracted from users. A comparative analysis was made between our proposed DNN classifier and some selected classical machine learning algorithms on the keystroke dynamics data. The data is split into five different data partition of training and testing. Results clearly indicated that the deep learningdense neural network has eliminated the feature extraction steps required by the classical algorithms and improved the overall authentication accuracy, as such, improved the security of the smartphone device. In addition, it is found that the propose deep learningdense neural network authentication scheme is more robust than the classical algorithms and has the potential to be fully implemented on smartphone to improve the security system of the mobile smartphone touch screen devices.

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Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

Dense Deep Neural Network Architecture for Keystroke Dynamics Authentication in Mobile Phone

Gabralla¹

2020

Adv. sci. technol. eng. syst. j.

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“…Deng and Zhong conducted a comparison between their work and the mobile keystroke biometric authentication model proposed by Ho . The authors compared deep neural network (DNN) to four classifiers, including Manhattan distance, random forest, Gaussian discriminant analysis, and SVM.…”

Section: Background and Related Workmentioning

confidence: 99%

Multimodal mobile keystroke dynamics biometrics combining fixed and variable passwords

Alshanketi

Traoré

Awad

2019

Security and Privacy

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Recent works have demonstrated the possibility to craft successful statistical attacks against keystroke dynamic biometric password. Those attacks leverage the possibility to capture several keystroke dynamics samples for a given password string, and then extract and use their distributional properties to craft the attack. These approaches are by design more likely to be successful when launched against fixed passwords, as several samples of the passwords can be captured through successive login sessions. Although the dynamics obtained from specific keys or key sequences for consecutive passwords slightly vary, by definition the distributional properties remain fairly stable for the same user. One way to thwart such that attack to use a variable password, also know as one‐time password (OTP). However, the fact that the keystroke dynamic OTP is different from one session to the other, makes it extremely difficult to reconstruct a valid biometric profile for a user. Modeling accurate keystroke dynamic OTP is challenging, due to the underlying variability and the sparse amount of information involved. We tackle the aformentioned challenge by presenting, in this paper, by presenting a multimodal approach tat combines fixed and variable keystroke dynamic biometric passwords. We investigate two different fusion models and evaluate our approach using a data set involving 100 different users, yielding encouraging performance results in terms of accuracy and resistance against statistical attacks.

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“…This method adapts the model by retraining the statistical classification algorithm which generates a user model by extracting a set of statistics from the training examples. This algorithm computes some statistics from the training examples (mean, median and standard deviation) for dwell and flight values for specified phrase in whole database [8] [10]. These statistical values represent the user model.…”

Section: A Statistical Algorithmmentioning

confidence: 99%

Strengthening user Authentication using Keystroke Dynamics

Shah¹,

Shah²,

Shah³

et al. 2015

IJERT

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In today's world, the technology has transitioned at such a great extent, that the mobile devices can do all the functions and tasks which were previously done by a personal digital assistant. The mobile phones have the ability to store sensitive and confidential information like digital certificates, financial records and company records, making them primary target for intruders. Thus to secure that, Personal Identification Number (PIN) is used currently, which has limitations from both end user and technological perspective. Therefore, there is a need for non-intrusive and stronger subscriber verification technique. This paper presents the viability of one such technique, the usage of keystroke dynamics to provide an additional layer of security along with PIN. In this paper, we discuss different approaches and methodologies to authenticate users based on their interactions with a mobile phone keypad, comprising a number of investigators into the ability of neural networks.

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Keystroke Dynamics Advances for Mobile Devices Using Deep Neural Network

Cited by 16 publications

References 26 publications

Dense Deep Neural Network Architecture for Keystroke Dynamics Authentication in Mobile Phone

Dense Deep Neural Network Architecture for Keystroke Dynamics Authentication in Mobile Phone

Multimodal mobile keystroke dynamics biometrics combining fixed and variable passwords

Strengthening user Authentication using Keystroke Dynamics

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