• KIISE Transactions on Computing Practices
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• v.22 no.3
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• pp.139-144
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• 2016

There exist many threats in cyber space, however current anti-virus software and other existing solutions do not effectively respond to malware that has become more complex and sophisticated. It was shown experimentally that it is possible for the proposed approach to provide an automatic execution environment for the detection of malicious behavior of active malware, comparing the virtual-machine environment with the real-machine environment based on user interaction. Moreover, the results show that it is possible to provide a dynamic analysis environment in order to analyze the intelligent malware effectively, through the comparison of malicious behavior activity in an automatic binary execution environment based on real-machines and the malicious behavior activity in a virtual-machine environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.368-378
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• 2018

This study examined the relationship between hostility and obsessive-compulsive symptoms to clarify the differential role of overt hostility and covert hostility on obsessive-compulsive symptoms. In addition, this study examined whether impulsivity has a moderating effect on the relationship between hostility measures and obsessive-compulsive symptoms. The Buss Durkee Hostility Inventory (BDHI), Revised Obsessive Compulsive Inventory (OCI-R), and Barratt Impulsivity Scale (BIS) were used to measure hostility, obsessive-compulsive symptoms, and impulsivity, respectively. Data were collected from 150 online university students and analyzed using the correlation and moderated multiple regression model. The result showed that overt hostility was positively correlated with obsessive thoughts; covert hostility was positively correlated with obsessive thoughts and compulsive behavior. In addition, the regression results, which set the hostility variables as the predicting variable, revealed covert hostility to increase obsessive thinking and compulsive behavior, whereas overt hostility had no significant effect on both variables. Impulsivity was found to function as a moderator in the prediction of covert hostility on obsessive thought. With the result, the implications and limitations of this study are discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2370-2378
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• 2016

Social Network Service(SNS) users are increasing globally. Within that trend, 'SNS attacking' victims are increasing in social network service space like KakaoStory, facebook, or Instargram as people damage others' personality or reputation. In this paper is to investigate and analyze awareness of negative behavior attributed to disinhibition effect with undergraduates who are the group of people using social network service the most diversely in smart environment and devise preventive measures to reduce social network service attacking victims and attackers. In social network service space, undergraduates are hardly aware of other people's personality, defamation, or invasion of privacy, and the level of guilt they feel towards social network service attacking is seriously low. To solve this problem, this study suggests preventive measures so that they can be equipped with awareness and regulations right for this social network service age and can prevent negative behavior resulted from disinhibition effect.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.167-174
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• 2019

The purpose of this study was to analyze the factors affecting the drinking behavior of adolescents. Based on this, it aims to suggest the practical and policy measures to prevent the drinking behavior of adolescents and to mediate / reduce them. We used binomial logistic analysis as an analysis method.As a result of this study, the individual factors affecting alcohol drinking were gender, smoking experience over the past year, sexual satisfaction, cyber delinquency, self-esteem, parental abuse, peer as family factors. Peer trust was significantly associated with attachment factors, and school adaptation factors were not found to be associated with alcohol drinking in adolescents. This suggests that multilateral efforts such as individuals, families, and communities are needed to mediate and reduce the drinking behavior of adolescents.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.754-762
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• 2010

Nowadays, email-based targeted attacks using malcode-bearing documents have been steadily increased. To improve the success rate of the attack and avoid anti-viruses, attackers mainly employ zero-day exploits and relevant social engineering techniques. In this paper, we propose an architecture of the email vaccine cloud system to prevent targeted attacks using malcode-bearing documents. The system extracts attached document files from email messages, performs behavior analysis as well as signature-based detection in the virtual machine environment, and completely removes malicious documents from the messages. In the process of behavior analysis, the documents are regarded as malicious ones in cases of creating executable files, launching new processes, accessing critical registry entries, connecting to the Internet. The email vaccine cloud system will help prevent various cyber terrors such as information leakages by preventing email based targeted attacks.

• Journal of KIISE
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• v.45 no.3
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• pp.294-302
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• 2018

With the emergence of the new service industry due to the development of information and communication technology, cyber space risks such as personal information infringement and industrial confidentiality leakage have diversified, and the security problem has emerged as a critical issue. In this paper, we propose a behavior-based anomaly detection method that is suitable for real-time and large-volume data analysis technology. We show that the proposed detection method is superior to existing signature security countermeasures that are based on large-capacity user log data according to in-company personal information abuse and internal information leakage. As the proposed behavior-based anomaly detection method requires a technique for processing large amounts of data, a real-time search engine is used, called Elasticsearch, which is based on an inverted index. In addition, statistical based frequency analysis and preprocessing were performed for data analysis, and the DBSCAN algorithm, which is a density based clustering method, was applied to classify abnormal data with an example for easy analysis through visualization. Unlike the existing anomaly detection system, the proposed behavior-based anomaly detection technique is promising as it enables anomaly detection analysis without the need to set the threshold value separately, and was proposed from a statistical perspective.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.225-238
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• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. In this paper, we propose a framework for user behavior analysis for bot detection in online games. Specifically, we focus on party play that reflects the social activities of gamers: In a Massively Multi-user Online Role Playing Game (MMORPG), party play log includes a distinguished information that can classify game users under normal-user and abnormal-user. That is because the bot users' main activities target on the acquisition of cyber assets. Through a statistical analysis of user behaviors in game activity logs, we establish the threshold levels of the activities that allow us to identify game bots. Also, we build a knowledge base of detection rules based on this statistical analysis. We apply these rule reasoner to the sixth most popular online game in the world. As a result, we can detect game bot users with a high accuracy rate of 95.92%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1224-1230
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• 2020

Since the spread of smart phones, interest in wearable devices has increased and diversified, and is closely related to the lives of users, and has been used as a method for providing personalized services. In this paper, we propose a method to detect the user's behavior by applying information from a 3-axis acceleration sensor and a 3-axis gyro sensor embedded in a smartphone to a convolutional neural network. Human behavior differs according to the size and range of motion, starting and ending time, including the duration of the signal data constituting the motion. Therefore, there is a performance problem for accuracy when applied to a convolutional neural network as it is. Therefore, we proposed a Time-Division Feature Fusion Convolutional Neural Network (TDFFCNN) that learns the characteristics of the sensor data segmented over time. The proposed method outperformed other classifiers such as SVM, IBk, convolutional neural network, and long-term memory circulatory neural network.

• Korean Journal of Social Welfare
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• v.57 no.4
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• pp.197-223
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• 2005

The purpose of this study is to examine the impact of spiritual commitment, social support and self-esteem for the deviant behaviors of middle-aged men from 40 to 59 years old. Further, this study seeks for providing basic data for designing social welfare service programs to intervene and prevent such deviant behaviors The findings of this study can be summarized as follows: The relationship among variables are as follows. First, spiritual commitment could directly affect the mid-life deviant behavior, and could cause influence indirectly through the self-esteem. Second, social support could not directly affect the mid-life deviant behavior, but could cause negative influence indirectly through the self-esteem. The significant is that this study triggered off interest in deviant behavior of middle-aged men. Especially, this study realized to us interest in spiritual commitment which has been not interested in the social work. This implicated that social support and self-esteem might effect to be deviant behavior of middle-aged men as well as deviant behavior of youth.

• Information Systems Review
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• v.18 no.2
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• pp.199-223
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• 2016

As social networking services (SNS) become widely used tools for maintaining social relationships, people use SNS to express themselves online. Users are free to form multiple characters in SNS because of online anonymity. This phenomenon causes SNS users to easily demonstrate multiple personas that are different from their identities in the real world. Therefore, this study focuses on online multi-personas that establish multiple fake identities in the SNS environment. The main objective of this study is to investigate factors that affect online multi-personas. Fake online identities can have various negative consequences such as cyber bullying, cyber vandalism, or antisocial behavior. Since the boundary between the online and offline worlds is fading fast, these negative aspects of online behavior may influence offline behaviors as well. This study focuses on teenagers who often create multi-personas online. According to previous studies, personal identities are usually established during a person's youth. Based on data on 664 teenage users, this study identifies four emotional factors, namely, closeness with others, relative deprivation, peer pressure and social norms. According to data analysis results, three factors (except closeness with others) have positive correlations with users' multi-personas. This study contributes to the literature by identifying the factors that cause young people to form online multi-personas, an issue that has not been fully discussed in previous studies. From a practical perspective, this study provides a basis for a safe online environment by explaining the reasons for creating fake SNS identities.