• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1027-1037
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• 2019

Conventional cyber-attack detection solutions are generally based on signature-based or malicious behavior analysis so that have had difficulty in detecting unknown method-based attacks. Since the various information occurring all the time reflects the state of the system, by modeling it in a steady state and detecting an abnormal state, an unknown attack can be detected. Since a variety of system information occurs in a string form, word embedding, ie, techniques for converting strings into vectors preserving their order and semantics, can be used for modeling and detection. Novelty Detection, which is a technique for detecting a small number of abnormal data in a plurality of normal data, can be performed in order to detect an abnormal condition. This paper proposes a method to detect system anomaly by cyber attack using embedding and novelty detection.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.179-191
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• 2024

With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.226-233
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• 2012

Recently, Like we saw with 3.4 DDoS Cyber Terror, a behavior of cyber terror becomes increasingly more complicated, sophisticated and larger, and there has been largely damage on industry, the general economy. For responding cyber terrors which occur in the future, we should recognize security holes of system which isn't exposed yet before attacker in advance as we anticipate and implement new technique of cyber attack which not exist hitherto. We design and implement a new technique of cyber attack; it seems to us that a server denies agent' service by altering value of registry in windows system. Network connections of agent are restricted to the new technique we suggest as the a value of registry is changed to a less value than a necessary value and there has happened packet loss by attacker.

• Journal of The Korean Association of Information Education
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• v.13 no.2
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• pp.193-204
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• 2009

This study examines the effects of cyber learning counseling based on self-reflective activities on learning motivation and habits of elementary students. From the above processes, following findings could be drawn: First, the cyber learning counseling based on self-reflective activities turned out to have positive effects on promoting the motivation of elementary students for learning. Positive impacts were found in 4 sub-factors of motivation for learning, that are, attention, relevance, conviction and satisfaction that were enhanced after cyber counseling for learning was given. Second, the cyber learning counseling based on self-reflective activities were found to have positive effects on changing elementary students' habit of learning. After cyber learning counseling was carried out, Positive impacts were visible in sub-factors of learning habit which are the behavioral indexes of applying learning skill and student-initiated behavior.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.4
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• pp.457-467
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• 2002

In order to simulate cyber attacks and predict network behavior by attacks, we should represent attributes of network components in the simulation model, and should express characteristics of systems that carry out various cyber attacks and defend from these attacks. To simulate how network load may change under the cyber attacks, we extended SSF[9, 10] that is process-based event-oriented simulation system. We added a firewall class and a packet manipulator into the SSFNet that is a component of SSF. The firewall class, which is related to the security, is to simulate cyber attacks, and the packet manipulator is a set of functions to write attack programs for the simulation. The extended SSFNet enables to simulate a network with the security systems and provides advantages that make easy to port already exsiting attack programs and apply them to the simulation evironment. We made a vitual network model to verify operations of the added classes, and simulated a smurf attack that is a representative denial of sevive attack, and observed the network behavior under the smurf attack. The results showed that the firewall class and packet manipulator developed in this paper worked normaly.

• Korean Journal of Human Ecology
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• v.19 no.2
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• pp.257-270
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• 2010

This study investigated preschoolers' happiness, behavior problems, and interpersonal problem solving strategies according to their sex and age, and the relationships among them. The subjects were 185 preschoolers (97 boys and 88 girls; 83 four-year-olds and 102 five-year-olds). Results showed that boys were higher in behavior problems (aggression) and forceful problem solving strategies than girls, while girls were higher in happiness (characteristics of self) than boys. Also, 4-year-old children were higher in forceful problem solving strategies than 5-year-olds. Children's happiness was negatively related to their internalizing and externalizing behavior problems. Behavior problems and interpersonal problem solving strategies of children were influenced by their happiness. These findings provide preliminary evidence that children's happiness may predict their behavior problems and interpersonal problem solving strategies.

• The Journal of Bigdata
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• v.1 no.1
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• pp.67-75
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• 2016

The study analyzed big data extracted from Google and social media to identify factors related to searches on cyber bullying in Korea and America. Korea's cyber bullying analysis was conducted social big data collected from online news sites, blogs, $caf{\acute{e}}s$, social network services and message for between January 1, 2011 and March 31, 2013. Google search trends for the search words of stress, exercise, drinking, and cyber bullying were obtained for January 1, 2004 and December 22, 2013. The main results of this study were as follows: first, the significant factors stress were cyber bullying that Korea more than America. Secondly, a positive relationship was found between stress and drinking, exercise and cyber bullying both Korea and America. Thirdly, significant differences were found all path both Korea and America. The study shows that both adults and teenagers are influenced in Korea. We need to develop online application that if cyber bullying behavior was predicted can intervene in real time because these actual cyber bullying-related exposure to psychological and behavioral characteristic.

• Journal of Families and Better Life
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• v.30 no.6
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• pp.1-11
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• 2012

We investigated the effects of maternal parenting behavior and social supports on children's problem behaviors. The participants are 148 elementary school children and their teachers from one elementary school in Seoul. The data were analyzed by using descriptive statistics, correlation analysis, t-test, and multiple regression. The major findings are summarized as follows: (1) there were differences in maternal parenting behavior(warmth), teacher support, and internal problem behaviors according to children's gender; (2) mothers's parenting behavior(warmth) and teachers' support explained children's overt problem behaviors; and (3) mothers' parenting behaviors(supervision) and friends' support explained children's internal problem behaviors. In conclusion, there were differences between the subscale of maternal parenting behavior and social supports influencing overt problem behaviors and internal problem behaviors.

• International Journal of Advanced Culture Technology
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• v.8 no.1
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• pp.152-156
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• 2020

The study is to examine the mediating effect of psychological empowerment on the causal relationship between high-performance work system and organizational citizenship behavior. This study collected data from public organizations that are responsible for social welfare provision in Daegu, Gyeongsangbuk-Do, and Pusan in South Korea. This study employs confirmatory factor analysis in Amos 21 to find the discriminant validity of all constructs and regression analysis by Baron and Kenny (1986) to test that psychological empowerment is a mediator of the causal relationship between high-performance work system and organizational citizenship behavior. As the result of the analysis, psychological empowerment has a significant mediating effect on the relationship between high-performance work system and organizational citizenship behavior. The study suggests to the managers in social welfare organizations how they need to enhance organizational citizenship behavior through psychological empowerment themselves.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.125-139
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• 2017

In this paper, we developed a framework to detect and predict insider information leakage by collecting and restoring network traffic. For automated behavior analysis, many meta information and behavior information obtained using network traffic collection are used as machine learning features. By these features, we created and learned behavior model, network model and protocol-specific models. In addition, the ensemble model was developed by digitizing and summing the results of various models. We developed a function to present information leakage candidates and view meta information and behavior information from various perspectives using the visual analysis. This supports to rule-based threat detection and machine learning based threat detection. In the future, we plan to make an ensemble model that applies a regression model to the results of the models, and plan to develop a model with deep learning technology.