• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.17-21
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• 2016

This paper investigates the importance of the computational overhead when machine learning methods, such as SVM, LASSO, AdaBoosting and AdaBagging, are used for automatic security classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2012-2030
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• 2021

Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1793-1802
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• 2017

In the recent years, IT and Network Technology has rapidly advanced environment in accordance with the needs of the times, the usage of the smart learning service is increasing. Smart learning is extended from e-learning which is limited concept of space and place. This system can be easily exposed to the various security threats due to characteristic of wireless service system. Therefore, this paper proposes the improvement methods of smart learning system security by use of faults analysis methods such as the FTA(Fault Tree Analysis) and FMECA(Failure Mode Effects and Criticality Analysis) utilizing the consolidated analysis method which maximized advantage and minimized disadvantage of each technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2866-2879
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• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. Previous studies on game bot detection have proposed many methods to find out discriminable behaviors of bots from humans based on the fact that a bot's playing pattern is different from that of a human. In this paper, we look at the chatting data that reflects gamers' communication patterns and propose a communication pattern analysis framework for online game bot detection. In massive multi-user online role playing games (MMORPGs), game bots use chatting message in a different way from normal users. We derive four features; a network feature, a descriptive feature, a diversity feature and a text feature. To measure the diversity of communication patterns, we propose lightly summarized indices, which are computationally inexpensive and intuitive. For text features, we derive lexical, syntactic and semantic features from chatting contents using text mining techniques. To build the learning model for game bot detection, we test and compare three classification models: the random forest, logistic regression and lazy learning. We apply the proposed framework to AION operated by NCsoft, a leading online game company in Korea. As a result of our experiments, we found that the random forest outperforms the logistic regression and lazy learning. The model that employs the entire feature sets gives the highest performance with a precision value of 0.893 and a recall value of 0.965.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.69-72
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• 2021

The article discusses the features of the use of distance technologies to intensify the learning process of students. The advantages and disadvantages of distance learning are shown. The role and functions of the teacher in distance learning have been adjusted. Information and methodological support for distance learning of students is proposed. Analyzed pedagogical, psychological, methodological and philosophical literature, educational standards, charters of higher educational institutions and other documents. Studied foreign experience in conducting classes using information technology.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.99-107
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• 2022

In the current technological society, where various technologies are converged into one and being transformed into new technologies, new cyber attacks are being made just as they keep pace with the changes in society. In particular, due to the convergence of various attacks into one, it is difficult to protect the system with only the existing security system. A lot of information is being generated to respond to such cyber attacks. However, recklessly generated vulnerability information can induce confusion by providing unnecessary information to administrators. Therefore, this paper proposes a mechanism to assist in the analysis of emerging cyberattack convergence technologies by providing differentiated vulnerability information to managers by learning documents using deep learning-based language learning models, extracting vulnerability information and classifying them according to the MITRE ATT&CK framework.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.21-29
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• 2024

With the development of ICT and network, security management of IT infrastructure that has grown in size is becoming very difficult. Many companies and public institutions are having difficulty managing system and network security. In addition, as the complexity of hardware and software grows, it is becoming almost impossible for a person to manage all security. Therefore, AI is essential for network security management. However, since it is very dangerous to operate an attack model in a real network environment, cybersecurity emulation research was conducted through reinforcement learning by implementing a real-life network environment. To this end, this study applied reinforcement learning to the network environment, and as the learning progressed, the agent accurately identified the vulnerability of the network. When a network vulnerability is detected through AI, automated customized response becomes possible.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.165-172
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• 2019

Machine-learning techniques have been actively employed to information security in recent years. Traditional rule-based security solutions are vulnerable to advanced attacks due to unpredictable behaviors and unknown vulnerabilities. By employing ML techniques, we are able to develop intrusion detection systems (IDS) based on anomaly detection instead of misuse detection. Moreover, threshold issues in anomaly detection can also be resolved through machine-learning. There are very few datasets for network intrusion detection compared to datasets for malicious code. KDD CUP 99 (KDD) is the most widely used dataset for the evaluation of IDS. Numerous studies on ML-based IDS have been using KDD or the upgraded versions of KDD. In this work, we develop an IDS model using CSE-CIC-IDS 2018, a dataset containing the most up-to-date common network attacks. We employ deep-learning techniques and develop a convolutional neural network (CNN) model for CSE-CIC-IDS 2018. We then evaluate its performance comparing with a recurrent neural network (RNN) model. Our experimental results show that the performance of our CNN model is higher than that of the RNN model when applied to CSE-CIC-IDS 2018 dataset. Furthermore, we suggest a way of improving the performance of our model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.471-476
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• 2011

As information and communication technologies (ICTs) has been developed, it is getting more important for internet users to consider many security issues (e.g., privacy protection). In this paper we claims that animation contents make a positive influence on teaching such computer securities, and investigate what features of animation contents will be the most important relationships with performance of the teaching process. Once we have obtained the survey results from students, two main features (i.e., characters and plots) of the animation contents have positively influenced the performance of e-learning systems.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.83-92
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• 2020

The current technology drain at small and medium-sized enterprises in Korea is very serious. According to the National Intelligence Service's survey data, 69 percent of technology leaks are made through employees of small and medium-sized enterprises. A document security system was introduced to compensate for the problem. However, small and medium-sized enterprises are not doing well due to their poor environment. Therefore, it proposes a document security system suitable for small businesses by developing a location information machine learning system that automatically creates a document security Green Zone through learning, and an ECM-based electronic document leakage prevention system that manages generated Green Zone information by reflecting it into the document authority system. And step by step, propose a universal solution through cloud services..