A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

Saini, Baljit Singh; Singh, Parminder; Nayyar, Anand; Kaur, Navdeep; Bhatia, Kamaljit Singh; El–Sappagh, Shaker; Hu, Jong Wan

doi:10.1109/access.2020.3008019

Cited by 26 publications

(16 citation statements)

References 47 publications

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“…Recently, several studies have applied keystroke dynamics-based authentication in various environments. These include studies based on keystroke dynamics from free text, in contrast to other work that utilized fixed length elements, such as passwords or PINs [ 10 , 11 ], as well as research that continuously classifies the user’s keystroke dynamics [ 11 , 12 , 13 ] and studies that consider various typing positions, such as sitting, walking, and relaxing positions [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

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Keystroke Dynamics-Based Authentication Using Unique Keypad

Choi

Lee

et al. 2021

Sensors

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Authentication methods using personal identification number (PIN) and unlock patterns are widely used in smartphone user authentication. However, these authentication methods are vulnerable to shoulder-surfing attacks, and PIN authentication, in particular, is poor in terms of security because PINs are short in length with just four to six digits. A wide range of research is currently underway to examine various biometric authentication methods, for example, using the user’s face, fingerprint, or iris information. However, such authentication methods provide PIN-based authentication as a type of backup authentication to prepare for when the maximum set number of authentication failures is exceeded during the authentication process such that the security of biometric authentication equates to the security of PIN-based authentication. In order to overcome this limitation, research has been conducted on keystroke dynamics-based authentication, where users are classified by analyzing their typing patterns while they are entering their PIN. As a result, a wide range of methods for improving the ability to distinguish the normal user from abnormal ones have been proposed, using the typing patterns captured during the user’s PIN input. In this paper, we propose unique keypads that are assigned to and used by only normal users of smartphones to improve the user classification performance capabilities of existing keypads. The proposed keypads are formed by randomly generated numbers based on the Mersenne Twister algorithm. In an attempt to demonstrate the superior classification performance of the proposed unique keypad compared to existing keypads, all tests except for the keypad type were conducted under the same conditions in earlier work, including collection-related features and feature selection methods. Our experimental results show that when the filtering rates are 10%, 20%, 30%, 40%, and 50%, the corresponding equal error rates (EERs) for the proposed keypads are improved by 4.15%, 3.11%, 2.77%, 3.37% and 3.53% on average compared to the classification performance outcomes in earlier work.

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Section: Related Workmentioning

confidence: 99%

“…In order to allow most data (i.e., about 95% data) of the normal user to be classified as data from the normal user, the sum of the mean of the calculated values above, mean_distance, and their standard deviation, std_distance, multiplied by 2 was set as the threshold. The threshold value was calculated using the following Equation (14).…”

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confidence: 99%

Keystroke Dynamics-Based Authentication Using Unique Keypad

Choi

Lee

et al. 2021

Sensors

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“…Multiple works on image and pattern analysis-based handwriting recognition and person identification have been reported. Saini et al in [5], proposed a three-step biometric authentication model based on sitting, walking, and relaxing patterns using mobile devices. Here, Random Forest and KNN are used for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

et al. 2021

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Handwriting analysis is playing an important role in user authentication or online writer identification for more than a decade. It has a significant role in different applications such as e-security, signature biometrics, e-health, gesture analysis, diagnosis system of Parkinson’s disease, Attention-deficit/hyperactivity disorders, analysis of vulnerable people (stressed, elderly, or drugged), prediction of gender, handedness and so on. Classical authentication systems are image-based, text-dependent, and password or fingerprint-based where the former one has the risk of information leakage. Alternatively, image processing and pattern-analysis-based systems are vulnerable to camera attributes, camera frames, light effect, and the quality of the image or pattern. Thus, in this paper, we concentrate on real-time and context-free handwriting data analysis for robust user authentication systems using digital pen-tablet sensor data. Most of the state-of-the-art authentication models show suboptimal performance for improper features. This research proposed a robust and efficient user identification system using an optimal feature selection technique based on the features from the sensor’s signal of pen and tablet devices. The proposed system includes more genuine and accurate numerical data which are used for features extraction model based on both the kinematic and statistical features of individual handwritings. Sensor data of digital pen-tablet devices generate high dimensional feature vectors for user identification. However, all the features do not play equal contribution to identify a user. Hence, to find out the optimal features, we utilized a hybrid feature selection model. Extracted features are then fed to the popular machine learning (ML) algorithms to generate a nonlinear classifier through training and testing phases. The experimental result analysis shows that the proposed model achieves more accurate and satisfactory results which ensure the practicality of our system for user identification with low computational cost.

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“…Phishing attack is another well-known threat to authentication using text passwords (Agaste et al, 2020), as a user might be convinced to use a phoney website and enter their login cardinalities.As depicted in Figure 1, authentication techniques can be categorized into three main categories, namely: (i) knowledge-based authentication, (ii) token-based authentication, and (ii) biometric-based authentication. In fact, biometric-based authentication is considered the securest and most reliable authentication technique since it depends on the personal characteristic of the end-user only (Saini et al, 2020). The widespread biometric methods utilized in authentication are scanning the fingerprint (Chanukya&Thivakaran, 2020), scanning retina (Srivastava, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…scanning face (Pramana et al, 2020), keystroke (Pramana et al, 2020). However, scanning the retina and figure are the most widely used biometric method being in authentication using smartphones (Saini et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Secure Two-factor Authentication Framework Based on Deep Learning

2022

JRSE

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Nowadays, many web sites make use of log in capabilities to permit users to get entry to protected information. From old days username and password has been the usual for accomplishing this. The hassle with this is that human beings generally tend to apply vulnerable password and frequently the equal password for more than one domain. Shared passwords between more than one domain, specifically vulnerable passwords, present high security risks, from this an attacker may take advantage of this to have unauthorized control for one or numerous of a user's accounts. This research intends to propose an efficient two-factor authentication for web page. The research presents drawbacks issues of two-factor web authentication based on multimodal biometric authentication.

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A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

Cited by 26 publications

References 47 publications

Keystroke Dynamics-Based Authentication Using Unique Keypad

Keystroke Dynamics-Based Authentication Using Unique Keypad

User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

A Secure Two-factor Authentication Framework Based on Deep Learning

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