Abstract: Analysing behavioural biometrics involves examining various user behaviours, such as the dominant hand used on a phone, the angle of device holding, typing speed and style, including keystroke rhythm and pressure applied, along with swipe and scroll patterns. Gait analysis further contributes by assessing an individual's walking pattern. Continuously monitoring these biometric traits and comparing them against established user profiles can significantly bolster security against identity theft and online fraud. However, it's paramount to strike a delicate balance between the security benefits and privacy concerns, ensuring the responsible use and safeguarding of user data. Our multi-modal authentication system harnesses both facial features and typing patterns, employing cutting-edge algorithms and real-time processing to deliver a seamless user authentication experience. Anti-spoofing measures are integrated to enhance system integrity, while comprehensive testing validates its effectiveness across a wide range of applications, from cybersecurity to access control. Continuous monitoring and updates are implemented to maintain optimal system performance, adapting to evolving security threats and user needs. By leveraging the distinctiveness of these behavioural biometrics, our system stands as a pioneering solution in enhancing security measures while prioritizing user privacy and usability.
Keywords: Behavioural biometrics, Keystroke rhythm, Finger pressure, Swipe patterns, Gait analysis.
Cite:
Rajesh N Kamath, Swathi.K.L., Vijetha Pai, Sneha C M, Lakshitha K Salian, "KEYSTROKE RHYTHM ANALYSIS FOR IDENTITY VERIFICATION", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.133105.
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DOI:
10.17148/IJARCCE.2024.133105
