Biometrie authentication using keystroke dynamics

Jadhav, Chandralekha; Kulkami, Siddhi; Shelar, Sagar; Shinde, Kaustubh; Dharwadkar, Nagaraj V.

doi:10.1109/i-smac.2017.8058304

Cited by 11 publications

(11 citation statements)

References 6 publications

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“…Six kinds of features are applied to represent keystroke behaviors: dwelling time (DT), flying time (FT), lifting time (LT), recovering time (RT), pressure value (P), and the pressure release speed (N). DT, FT, and P were popularly used in previous keystroke studies [45,[68][69][70]. LT, RT, and N are added because they represent the degree of energy concentration [71][72].…”

Section: Feature Extractionmentioning

confidence: 99%

Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition

Qi,

Jia,

Feng

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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Emotion recognition, a process of automatic cognition of human emotions, has great potential to improve the degree of social intelligence. Among various recognition methods, Emotion recognition based on touch event's temporal and force information receives global interests. Although previous studies have shown promise in the field of keystroke-based emotion recognition, they are limited by the need for long-term text input and the lack of high-precision force sensing technology, hindering their real-time performance and wider applicability. To address this issue, in this paper, a piezoelectric-based keystroke dynamic technique is presented for quick emotion detection. The nature of piezoelectric materials enables high-resolution force detection. Meanwhile, the data collecting procedure is highly simplified because only the password entry is needed. International Affective Digitized Sounds (IADS) are applied to elicit users ' emotions, and a PAD emotion scale is used to evaluate and label the degree of emotion induction. A Random Forest (RF) based algorithm is used in order to reduce the training dataset and improve algorithm portability. Finally, an average recognition accuracy of 79.37% of 4 emotions (happiness, sadness, fear, disgust) is experimentally achieved. The proposed technique improves the reliability and practicability of emotion recognition in realistic social systems.

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Section: Feature Extractionmentioning

confidence: 99%

Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition

Qi,

Jia,

Feng

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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“…Factors such as -what the user knows‖ (inherent), -what the user has‖ (possession), and -what the user is‖ (biometric) have served as additional authentication methods [32]. In this regard, biometric features unique to individuals, ranging from physiological characteristics (e.g., fingerprints and iris, hand, and face patterns) to behavioral characteristics (e.g., KD, www.ijacsa.thesai.org mouse movements, gait, and handwriting), have served to identify users [3]. Many such biometric approaches tend either to be expensive or to place heavy demands on computer hardware, making them inappropriate for most users [35].…”

Section: B Multifactor Authentication (Mfa)mentioning

confidence: 99%

“…Jadhav Kulkami, Shelar, Shinde, and Dharwadkar [3] proposed an ML-based authentication model that uses the static approach of keystroke dynamics to recognize and authenticate users accessing the system based on their unique keystroke profiles with respect to the flight, dwell, press, press-to-press, and release-to-release time and achieved an FAR and an FRR of 1% and 4%, respectively. Gupta, Khanna, Jagetia, Sharma, Alekh, and Chouldhary [35] proposed a high-efficiency authentication system combining two methods to make keystroke biometrics less susceptible to forgery and more usable and reported that the system efficiently implemented secure authentication with the advantage of ease of implementation since all that is required is the installation of software on any workstation.…”

Section: Behavioral Authentication Using MLmentioning

confidence: 99%

“…Ensuring data protection and privacy through effective and efficient authentication methods is not only a priority for individuals, organizations, and countries but also a context for promoting incremental and radical innovation. Data-protection authorities must acquire new technologies and use them effectively to regulate personal information practices so as to meet present and future challenges, for the technology advances in step with the security threats [2][3]. Hence, cybersecurity attacks have been increasing exponentially and rendering existing detection mechanisms insufficient [4].…”

Section: Introductionmentioning

confidence: 99%

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A Secure and Scalable Behavioral Dynamics Authentication Model

Oladipo,

Awotunde,

Nicho

et al. 2023

IJACSA

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Various authentication methods have been proposed to mitigate data breaches. However, the increasing frequency of data breaches and users' lack of awareness have exposed traditional methods, including single-factor passwordbased systems and, two-factor authentication systems, to vulnerabilities against attacks. While behavioral authentication holds promise in tackling these issues, it faces challenges concerning interoperability between operating systems, the security of behavioral data, accuracy enhancement, scalability, and cost. This research presents a scalable dynamic behavioral authentication model utilizing keystroke typing patterns. The model is constructed around five key components: humancomputer interface devices, encryption of behavioral data, consideration of the authenticator's emotional state, incorporation of cross-platform features, and proposed implementation solutions. It addresses potential typing errors and employs data encryption for behavioral data, achieving a harmonious blend of usability and security by leveraging keyboard dynamics. This is accomplished through the implementation of a web-based authentication system that integrates Convolutional Neural Networks (CNNs) for advanced feature engineering. Keystroke typing patterns were gathered from participants and subsequently employed to evaluate the system's keystroke timing verification, login ID verification, and error handling capabilities. The web-based system uniquely identifies users by merging their username-password (UN-PW) credentials with their keyboard typing patterns, all while securely storing the keystroke data. Given the achievement of a 100% accuracy rate, the proposed Behavioral Dynamics Authentication Model (BDA) introduces future researchers to five scalable constructs. These constructs offer an optimal combination, tailored to the device and context, for maximizing effectiveness. This achievement underscores its potential applications in the realm of authentication.

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“…In the mobile keystroke dynamic biometric authentication approach proposed recently by Jadhav et al, timing and pressure features were extracted, and analyzed by computing the mean and the SD to classify the users based on predetermined threshold. The approach was evaluated by collecting 20 password samples from four subjects.…”

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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Biometrie authentication using keystroke dynamics

Cited by 11 publications

References 6 publications

Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition

Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition

A Secure and Scalable Behavioral Dynamics Authentication Model

Multimodal mobile keystroke dynamics biometrics combining fixed and variable passwords

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