• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.824-827
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• 2005

The need to be able to identify other individual human beings is fundamental to the security and has been true since the beginning of human history. Physical or behavioral characteristics (finger-scan, face-scan, voice, palm, iris, retina, signature, human DNA, keystroke, vain, gait etc.) of a person are used to authenticate the person. The biometric technologies allow for a greater reliability of authentication as compared with password systems for physical access, network security, e-commerce, and so on. In this paper, we describe various technologies, market analysis, multi-modal system, standardization, privacy issue and future prospect of biometrics.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.75-81
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• 2023

Since the introduction of smartphones, the introduction of charging cable infrastructure that can be used for public use is underway. Thanks to this, people use public cables comfortably without doubt, but most people are not aware of the dangers of public cables. These public cables can lead to infringement accidents such as personal information exposure due to the development of hacking cables, and in the worst case, hackers can take control of smartphones and laptops. This study analyzed the operating principles and attack principles of hacking cables that seem like these general charging cables, but contain malicious scripts or hardware inside. In addition, physical and logical countermeasures were considered based on the analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2844-2861
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• 2023

Internet is the most prevailing word being used nowadays. Over the years, people are becoming more dependent on the internet as it makes their job easier. This became a part of everyone's life as a means of communication in almost every area like financial transactions, education, and personal-health operations. A lot of data is being converted to digital and made online. Many researchers have proposed different authentication factors - biometric and/or non-biometric authentication factors - as the first line of defense to secure online data. Among all those factors, passwords and passphrases are being used by many users around the world. However, the usability of these factors is low. Also, the passwords are easily susceptible to brute force and dictionary attacks. This paper proposes the generation of a novel passcode from the hybrid authentication factor - sound. The proposed passcode is evaluated for its strength to resist brute-force and dictionary attacks using the Shannon entropy and Passcode (or password) entropy formulae. Also, the passcode is evaluated for its usability. The entropy value of the proposed is 658.2. This is higher than that of other authentication factors. Like, for a 6-digit pin - the entropy value was 13.2, 101.4 for Password with Passphrase combined with Keystroke dynamics and 193 for fingerprint, and 30 for voice biometrics. The proposed novel passcode is far much better than other authentication factors when compared with their corresponding strength and usability values.

• Journal of the Ergonomics Society of Korea
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• v.35 no.4
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• pp.277-291
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• 2016

Objective: The aim of this study is to propose a unit touch gesture model, which would be useful to predict the performance time on mobile devices. Background: When estimating usability based on Model-based Evaluation (MBE) in interfaces, the GOMS model measured 'operators' to predict the execution time in the desktop environment. Therefore, this study used the concept of operator in GOMS for touch gestures. Since the touch gestures are comprised of possible unit touch gestures, these unit touch gestures can predict to performance time with unit touch gestures on mobile devices. Method: In order to extract unit touch gestures, manual movements of subjects were recorded in the 120 fps with pixel coordinates. Touch gestures are classified with 'out of range', 'registration', 'continuation' and 'termination' of gesture. Results: As a results, six unit touch gestures were extracted, which are hold down (H), Release (R), Slip (S), Curved-stroke (Cs), Path-stroke (Ps) and Out of range (Or). The movement time predicted by the unit touch gesture model is not significantly different from the participants' execution time. The measured six unit touch gestures can predict movement time of undefined touch gestures like user-defined gestures. Conclusion: In conclusion, touch gestures could be subdivided into six unit touch gestures. Six unit touch gestures can explain almost all the current touch gestures including user-defined gestures. So, this model provided in this study has a high predictive power. The model presented in the study could be utilized to predict the performance time of touch gestures. Application: The unit touch gestures could be simply added up to predict the performance time without measuring the performance time of a new gesture.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.857-869
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• 2016

It is a well-known fact that password based authentication system has been threatened for crucial data leakage through monitoring key log. Recently, to prevent this type of attack using keystroke logging, virtual onscreen keyboards are widely used as one of the solutions. The virtual keyboards, however, also have some crucial vulnerabilities and the major weak point is that important information, such as password, can be exposed by tracking the trajectory of the mouse cursor. Thus, in this paper, we discuss the vulnerabilities of the onscreen keyboard, and present hypothetical attack scenario and a method to crack passwords. Finally to evaluate the performance of the proposed scheme, we demonstrate an example experiment which includes attacking and cracking by utilizing password dictionary and analyze the result.

• Journal of Korea Game Society
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• v.16 no.2
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• pp.7-16
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• 2016

In this paper, we propose a method for effectively detecting specific behavior. The proposed method detects outlying behavior based on the game players' characteristics. These characteristics are captured non-invasively in a general game environment and add keystroke based on repeated pattern. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players was used to analyze high-dimensional game-player data for a detection effect of repeated behaviour pattern. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. In addition, Repeated behaviour pattern can be analysed possible. The proposed method can also be used for feedback and quantification about analysis of various interactive content provided in PC environments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2081-2085
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• 2013

The purpose of this study is to investigate the usability and user interface in different types of mobile phones. KLM(key-stroke-level model) model estimation and observation values were calculated to perform 11 functional factors in 4 ordinary mobile phones and 4 smart phones. In results, it was found that the usability of ordinary phone is superior to smart phone in basic and simple function likes phone call. On the contrary, the usability of smart phone is superior to memorizing and picturing function. Current results can be used to anticipate the future effort to improve user interface design especially in smart phone.