• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.8
• /
• pp.4076-4092
• /
• 2019

Nowadays, most users access internet through mobile applications. The common way to authenticate users through websites forms is using passwords; while they are efficient procedures, they are subject to guessed or forgotten and many other problems. Additional multi modal authentication procedures are needed to improve the security. Behavioral authentication is a way to authenticate people based on their typing behavior. It is used as a second factor authentication technique beside the passwords that will strength the authentication effectively. Keystroke dynamic rhythm is one of these behavioral authentication methods. Keystroke dynamics relies on a combination of features that are extracted and processed from typing behavior of users on the touched screen and smart mobile users. This Research presents a novel analysis in the keystroke dynamic authentication field using two features categories: timing and no timing combined features. The proposed model achieved lower error rate of false acceptance rate with 0.1%, false rejection rate with 0.8%, and equal error rate with 0.45%. A comparison in the performance measures is also given for multiple datasets collected in purpose to this research.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
• /
• 2006.06a
• /
• pp.202-206
• /
• 2006

오늘날 패스워드 인증과 키 분배는 컴퓨터 환경에서 중요하다. 패스워드 기반의 시스템은 패스워드를 사용자가 기억하기 쉽다는 장점 때문에 널리 사용 되고 있다. 하지만, 패스워드는 작은 공간에서 선택되어지기 때문에 패스워드 추측 공격을 포함한 다양한 공격에 취약점을 나타낸다. 본 논문에서는 최근에 제안된 새로운 패스워드 인증 기법을 분석하고, 서버 위장 공격, 서버 속임 공격과 패스워드 추측 공격에 취약하다는 것을 보인다. 또한, 패스워드 기반의 기법을 설계할 때는 주의해야 한다는 점에 대해 논하고, IEEE 1363.2와 같은 표준을 사용해 CK 프로토콜을 강하게 하는 법에 대해 간단히 보인다.

• Journal of Korea Multimedia Society
• /
• v.11 no.8
• /
• pp.1129-1137
• /
• 2008

E-learning as a new education trend is being applied not only to cyber school but also various education fields such as employee training for companies or interactive learning for consumers. Users of the E-learning can take online tests individually anywhere, to evaluate their achievement level. Because users who are taking the online tests may show their own IDs or passwords to others, the possibility of cheating is very high. Therefore, it is very important to authenticate the users. In this paper, we propose an adaptive-keyboard authentication method which depends on user behavior patterns through the use of IDs and passwords. This method does not need any additional devices or special effort. An adaptive method to update patterns in which IDs and passwords are entered was previously suggested and this new method has proved to be better than previous methods through simulations and implementation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.6
• /
• pp.3055-3073
• /
• 2019

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.