Adaptive Mobile Keystroke Dynamic Authentication Using Ensemble Classification Methods

2018 ◽  
pp. 38-49 ◽  
Author(s):  
Faisal Alshanketi ◽  
Issa Traoré ◽  
Awos Kanan ◽  
Ahmed Awad
Keyword(s):  
Ensemble Classification ◽  
Classification Methods ◽  
Keystroke Dynamic ◽  
Dynamic Authentication

scholarly journals Analyzing the Effectiveness of Touch Keystroke Dynamic Authentication for the Arabic Language

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Suliman A. Alsuhibany ◽  
Afnan S. Almuqbil
Keyword(s):  
Arabic Language ◽  
Error Rates ◽  
Free Text ◽  
Classification Methods ◽  
Traditional Methods ◽  
Digital Devices ◽  
Android Platform ◽  
Authentication Technique ◽  
Keystroke Dynamic ◽  
Dynamic Authentication

The keystroke dynamic authentication (KDA) technique was proposed in the literature to develop a more effective authentication technique than traditional methods. KDA analyzes the rhythmic typing of the owner on a keypad or keyboard as a source of verification. In this study, we extend the findings of the system by analyzing the existing literature and validating its effectiveness in Arabic. In particular, we examined the effectiveness of the KDA system in Arabic for touchscreen-based digital devices using two KDA classes: fixed and free text. To this end, a KDA system was developed and applied to a selected device operating on the Android platform, and various classification methods were used to assess the similarity between log-in and enrolment sessions. The developed system was experimentally evaluated. The results showed that using Arabic KDA on touchscreen devices is possible and can enhance security. It attains a higher accuracy with average equal error rates of 0.0% and 0.08% by using the free text and fixed text classes, respectively, implying that free text is more secure than fixed text.

Using ensemble classification methods in lung cancer disease*

2019 ◽  
Author(s):  
Mohamed Hosni ◽  
Juan M. Carrillo-de-Gea ◽  
Ali Idri ◽  
Jose Luis Fernandez-Aleman ◽  
Jose A. Garcia-Berna
Keyword(s):  
Lung Cancer ◽  
Ensemble Classification ◽  
Classification Methods ◽  
Cancer Disease

scholarly journals Keystroke Dynamic Authentication Using Combined MHR (Mean of Horner’s Rules) and Standard Deviation

2018 ◽  
pp. 13-18 ◽  
Author(s):  
Didih Rizki Chandranegara ◽  
Fauzi Dwi Setiawan Sumadi
Keyword(s):  
Standard Deviation ◽  
High Accuracy ◽  
Previous Method ◽  
Acceptance Rate ◽  
Biometric Authentication ◽  
False Acceptance Rate ◽  
False Acceptance ◽  
Keystroke Dynamic ◽  
Dynamic Authentication

Keystroke Dynamic Authentication used a behavior to authenticate the user and one of biometric authentication. The behavior used a typing speed a character on the keyboard and every user had a unique behavior in typing. To improve classification between user and attacker of Keystroke Dynamic Authentication in this research, we proposed a combination of MHR (Mean of Horner’s Rules) and standard deviation. The results of this research showed that our proposed method gave a high accuracy (93.872%) than the previous method (75.388% and 75.156%). This research gave an opportunity to implemented in real login system because our method gave the best results with False Acceptance Rate (FAR) is 0.113. The user can be used as a simple password and ignore a worrying about an account hacking in the system.

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