• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.549-555
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• 2019

According to a recent report by anti-virus vendors, the number of new and modified malware increased exponentially. Therefore, malware analysis research using machine learning has been actively researched in order to replace passive analysis method which has low analysis speed. However, when using supervised learning based machine learning, many studies use low-reliability malware family name provided by the antivirus vendor as the label. In order to solve the problem of low-reliability of malware label, this paper introduces a new labeling technique, "Unified Labeling", and further verifies the malicious behavior similarity through the feature analysis of the fine-grained method. To verify this study, various clustering algorithms were used and compared with existing labeling techniques.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.3
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• pp.101-106
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• 2004

Recently, a network defense simulator becomes essential in studying cyber incidents because the cyber terror become more and more interesting. The network defense simulator is a tool to estimate damages and an effectiveness of a defense mechanism by modeling network intrusions and defense mechanisms. Using this tool, users can find efficient ways of preventing a cyber terror and recovering from the damage. Previous simulators start the simulation after entire scenario has made and been loaded to simulation engine. However, in this way it can't model human judgement and behavior, and it can't simulate the real cyber terror very well. In this paper, we have added a dynamic simulation component to our previous network security simulator. This component improved accurate modeling of network intrusions and defense behaviors. We have also proposed new modified architecture of the simulation system. Finally we have verified correct simulation results from stammer worn simulation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.6
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• pp.37-46
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• 2006

It is known to be hard to apply cryptographic one-way functions to the recognition system using bio-information directly. As one of the solutions about that problem there is a permutation transformation scheme. However, they did not show my algorithmic behavior or any performance analysis of the transformation by experiment. In this paper, by showing the recognition ratio of the transformed scheme by experiment, we prove that that scheme is sound. Also, we adopt their transformation to LDA(Linear Discriminant Analysis) to show the experimental results. In the negative side, we introduce a new type of attack against the permutation transformation schemes. finally, we briefly mention a generalization of the permutation transformation for countermeasure of the attack at the end of this paper.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.5
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• pp.59-67
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• 2010

In the paper, we proposed an IM-ACM(Insider Misuse-Access Control Model) for preventing illegal information leakage by insider who exploits his legal rights in the ubiquitous computing environment. The IM-ACM can monitor whether insider uses data rightly using misuse monitor add to CA-TRBAC(Context Aware-Task Role Based Access Control) which permits access authorization according to user role, context role, task and entity's security attributes. It is difficult to prevent information leakage by insider because of access to legal rights, a wealth of knowledge about the system. The IM-ACM can prevent the information flow between objects which have the different security levels using context role and security attributes and prevent an insider misuse by misuse monitor which comparing an insider actual processing behavior to an insider possible work process pattern drawing on the current defined profile of insider's process.

• Convergence Security Journal
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• v.20 no.5
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• pp.11-17
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• 2020

With the increase in cases of using Bluetooth devices used in the electric vehicle charging systems, security issues are also raised. Although various technical efforts have beed made to enhance security of bluetooth technology, various attack methods exist. In this paper, we propose an intelligent Bluetooth intrusion detection system based on a well-known machine learning method, Hidden Markov Model, for the purpose of detecting intelligently representative Bluetooth attack methods. The proposed approach combines packet types of H4, which is bluetooth transport layer protocol, and the transport directions of the packet firstly to represent the behavior of current traffic, and uses the temporal deployment of these combined types as the final input features for detecting attacks in real time as well as accurate detection. We construct the experimental environment for the data acquisition and analysis the performance of the proposed system against obtained data set.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.169-174
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• 2021

While the importance of information security policies is heightened, numerous empirical studies have been conducted to investigate the factors that influence employee's willingness to comply organizational security policies. Some of those studies, however, were not consistent and even contradictory each other. Synthesizing research outcomes has been resulted as qualitative literature reviews or quantitative analysis on individual effect sizes, which leads to meta-analyze on whole research model. This study investigated 28 empirical research based on the deterrence theory with sanction certainty, severity and celerity. The analysis with random effect model resulted in well-fitted research model as well as all of significant paths in the model. Future research can include informal deterrent factors and contextual factors as moderator variables.

• The Journal of Bigdata
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• v.6 no.2
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• pp.99-108
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• 2021

Recently, unexpected and more advanced cyber medical treat attacks are on the rise. However, in responding to various patterns of cyber medical threat attack, rule-based security methodologies such as physical blocking and replacement of medical devices have the limitations such as lack of the man-power and high cost. As a way to solve the problems, the medical community is also paying attention to artificial intelligence technology that enables security threat detection and prediction by self-learning the past abnormal behaviors. In this study, there has collecting and learning the medical information data from integrated Medical-Information-Systems of the medical center and introduce the research methodology which is to develop the AI-based Net-Working Behavior Adaptive Information data. By doing this study, we will introduce all technological matters of rule-based security programs and discuss strategies to activate artificial intelligence technology in the medical information business with the various restrictions.

• Korean Journal of Human Ecology
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• v.6 no.1
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• pp.79-88
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• 1997

The postpurchase process is the phase that consumers evaluate products or services while using them. And then, they may experience satisfaction/dissatisfaction and emotions elicited products or services. The satisfaction and emotions may be important concepts in consumer behavior such as brand attitude, purchase intentions and using. The purpose of this study was to examine the effects of emotions elicited clothing products on product satisfaction and using in postpurchase. The subjects in this study were selected 500 females. The data were collected by using questionnaires, and 431 data were analysed. The results were as follows: The emotions in postpurchase process were consisted of three dimensions; unpleasure, pleasure, security/activity. These three emotions had the causal effects on product satisfaction significantly, and the unpleasure influenced stronger than others on satisfaction. In addition, the emotions were related to using and the ownership of clothing products. The emotions of pleasure and the security/activity influenced on the ownership, and the security/activity and unpleasure influenced on using clothing products. Therefore, the results in this study imply that consumption emotions elicited clothing products would be multidimensional, and the emotional factors would be important indicators for explaining the satisfaction. The positive affects were related to using clothes, which is, the consumer would have fulfilled their hedonic desires by wearing clothes. Also, the positive affects would be the basis for marketing strategies of sales promotions in clothing products.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.376-379
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• 2017

전 세계적으로 악성코드는 하루 100만개 이상이 새롭게 발견되고 있으며, 악성코드 발생량은 해마다 증가하고 있는 추세이다. 공격자는 보안장비에서 악성코드가 탐지되는 것을 우회하기 위해 기존 악성코드를 변형한 변종 악성코드를 주로 이용한다. 변종 악성코드는 자동화된 제작도구나 기존 악성코드의 코드를 재사용하므로 비교적 손쉽게 생성될 수 있어 최근 악성코드 급증의 주요 원인으로 지목되고 있다. 본 논문에서는 대량으로 발생하는 악성코드의 효과적인 대응을 위한 행위기반 악성코드 프로파일링 시스템 프로토타입을 제안한다. 동일한 변종 악성코드들은 실제 행위가 유사한 특징을 고려하여 악성코드가 실행되는 과정에서 호출되는 API 시퀀스 정보를 이용하여 악성코드 간 유사도 분석을 수행하였다. 유사도 결과를 기반으로 대량의 악성코드를 자동으로 그룹분류 해주는 시스템 프로토타입을 구현하였다. 악성코드 그룹별로 멤버들 간의 유사도를 전수 비교하므로 그룹의 분류 정확도를 객관적으로 제시할 수 있다. 실제 유포된 악성코드를 대상으로 악성코드 그룹분류 기능과 정확도를 측정한 실험에서는 평균 92.76%의 분류 성능을 보였으며, 외부 전문가 의뢰에서도 84.13%로 비교적 높은 분류 정확도를 보였다.

• Convergence Security Journal
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• v.12 no.4
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• pp.77-84
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• 2012

The control network has been operated in a closed. But it changes to open to external for business convenience and cooperation with several organizations. As the way of connecting with user extends, the risk of control network gets high. Thus, in this paper, proposed the technique of an anomalous event detection using white-list for control network security and minimizing the cyber threats. The proposed method can be collected and cataloged of only normal data from traffic of internal network, control network and field devices. Through way to check the this situation, we can separate normal and abnormal behavior.