• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.956-961
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• 2006

Keystroke time intervals can be a discriminating feature in the verification and identification of computer users. This paper presents a comparison result obtained using several classification methods including k-NN (k-Nearest Neighbor), back-propagation neural networks, and Bayesian classification for keystroke identification. Performance of k-NN classification was best with small data samples available per user, while Bayesian classification was the most superior to others with large data samples per user. Thus, for web-based on-line identification of users, it seems to be appropriate to selectively use either k-NN or Bayesian method according to the number of keystroke samples accumulated by each user.

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

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.19-25
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• 2016

There are many free applications that need users to sign up before they can use the applications nowadays. It is difficult to choose a suitable password for your account. If the password is too complicated, then it is hard to remember it. However, it is easy to be intruded by other users if we use a very simple password. Therefore, biometric-based approach is one of the solutions to solve the issue. The biometric-based approach includes keystroke dynamics on keyboard, mice, or mobile devices, gait analysis and many more. The approach can integrate with any appropriate machine learning algorithm to learn a user typing behavior for authentication system. Preprocessing phase is one the important role to increase the performance of the algorithm. In this paper, we have proposed ensemble-by-session (EBS) method which to operate the preprocessing phase before the training phase. EBS distributes the dataset into multiple sub-datasets based on the session. In other words, we split the dataset into session by session instead of assemble them all into one dataset. If a session is considered as one day, then the sub-dataset has all the information on the particular day. Each sub-dataset will have different information for different day. The sub-datasets are then trained by a machine learning algorithm. From the experimental result, we have shown the improvement of the performance for each base algorithm after the preprocessing phase.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.202-206
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• 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 the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.871-883
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• 2016

It has been steadily increasing to use a wireless keyboard via Radio Frequency which is the input device. Especially, wireless keyboards that use 2.4 GHz frequency band are the most common items and their vulnerabilities have been reported since 2010. In this paper, we propose a 2.4 GHz wireless keyboard keystroke analysis and injection system based on the existing vulnerability researches of the Microsoft 2.4 GHz wireless keyboards. This system is possible to control on the remote. We also show that, via experiments using our proposed system, sensitive information of user can be revealed in the real world when using a 2.4 GHz wireless keyboard.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.6
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• pp.656-661
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• 2015

The goal of this paper is to analyze the performance of a word autocomplete system for mobile devices such as smartphones, tablets, and PCs. The proposed system automatically completes a partially typed string into a full word, reducing the time and effort required by a user to enter text on these devices. We collect a large amount of data from Twitter and develop both unigram and bigram dictionaries based on the frequency of words. Using these dictionaries, we analyze the performance of the word autocomplete system and devise a keystroke profit rate and recovery rate as new evaluation metrics that better describe the characteristics of the word autocomplete problem compared to previous measures such as the mean reciprocal rank or recall.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.652-655
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• 2006

최근 핸드폰 같은 휴대용 단말기의 용도는 통화 이외에도 예금, 증권, 결제, 신원확인 등과 같은 다양한 어플리케이션으로 발전하고 있다. 본 논문에서는 키스트로크 기반의 사용자 인증을 이용한 모바일 보안강화 방안에 대하여 논의한다. 키스트로크 다이나믹스 패턴분석은 사용자가 특정 문자열을 타이핑할 때의 입력 패턴을 고려한 분석 방법이다. 본 연구는 휴대단말기의 짧은 암호사용의 문제점을 극복하기 위하여 인공리듬과 템포 큐를 활용하였으며, 높은 분류 성능을 보여주었다.

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

• Cartoon and Animation Studies
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• s.40
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• pp.337-365
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• 2015

The objective of this study proposes the new user's addiction model in 'Social Network Games' (SNGs). Research model is derived from the separation of two characteristics. First one is logical characteristics that includes 'Functional' (F), 'Keystroke' (K), and 'Goal' (G). Second one is feeling characteristics that consists a few factors such as 'Emotion' (E), 'Social' (S), and 'Affection' (A). For the pre-test, a total of 30 participants responded to survey in order to inspect the fitness of research questionnaire, roughly validity of the proposed model, and the direction of this reseach. After that for the main test, a total 300 users participated in this research. The final number of effective participants were 261 because 39 were insincere respondents and without playing SNGs who were excluded. Then we examined the measurement model by performing 'Partial Least Squares - Structural Equation Modeling' (PLS-SEM) analysis to test the research hypothesis empirically. The results of the measurement and structural model test lend support to the proposed research model by providing a good fit to the construct data. Interestingly, the model showed the significant effects of the interaction between eleven hypothesis(H1,H2,H3,H4,H5,H6,H7,H8,H9,H10, H12). Only one hypothesis decision t-value not supported that is involved the relationship between SNGs Addiction and Keystroke, H11(1.193). This research expect to contributes to an exploratory SNGs research to clarify the base of addition and will aids understanding of users' behavior associated with SNGs development.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.11
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• pp.1004-1011
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• 2016

In this paper, we suggested the methodology of analyzing and reconstructing of measured electromagnetic emanations from the Micro Controller Unit(MCU) chip of Universal Serial Bus(USB) keyboard. By analyzing electromagnetic emanations, entered information is found at keystroke and furthermore, information security problems such as personal information leakage and eavesdropping can be arisen. USB keyboards make the radiated signal according to the signal transmission mechanism. Electromagnetic emanations were measured by log periodic antenna and wideband receiver and were analyzed by signal processing algorithm.