• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.249-267
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• 2018

The US cybersecurity workforce policy is being pursued comprehensively and systematically, based on the NICE established initiated in 2010. Security Technologies, one of the eight areas of Advanced Technology Education(ATE) of the National Science Foundation(NSF) included in the STEM. This policy has been comprehensively promoted in conjunction with NICE, and this security technology field is operated with five detailed programs. In this paper, we examine in detail five cybersecurity workforce development programs supported by ATE, and compare them with the current status cultivation of cybersecurity workforce in Korea. After finding out the problems and improvements by comparison with the current situation of cybersecurity workforce development in Korea, we propose several implementations of nation-wide strategies for cultivating new cybersecurity workforce in Korea.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.973-985
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• 2021

South Korea has been guaranteed the efficiency and the convenience of administrative work based on long-term experience and well-established ICT infrastructure. Vice versa, South Korea is always exposed to various scale cyber-attacks. It is an important element of national competitiveness to secure cybersecurity resilience and response in the public sector. For this, the well-trained cybersecurity professionals' retention and support for their capacity development through retraining are critical. As the Special Act on Balanced National Development, most public agencies moved to provincial areas, but the provincial areas are not ready for this, thus the workforce can't get enough retaining courses. We study to analyze whether there is a gap in cybersecurity educational opportunities or needs in the public sector depending on regions, institution type, and personal traits. This paper aims to suggest solutions for the cybersecurity education gap in the public sector based on the empirical analysis results.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.65-70
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• 2021

According to the trend of increasingly sophisticated cyber threats, the need for technology research that can be applied to cyber security personnel management and training systems is constantly being raised not only overseas but also in Korea. Previously, the US and UK have already recognized the need and have been steadily conducting related research from the past. In the United States, by encouraging applications based on related research (NICE Cybersecurity Workforce Framework) and disclosing successful use cases to the outside, it is laying the groundwork for profiling cyber security experts. However in Korea, research on cyber security expert training and profiling is insufficient compared to other countries. Therefore, in this study, in order to create a system suitable for the domestic situation, research and analysis of cases in the United States and the United Kingdom were conducted over the past few years, and based on this, a prototype was produced for the study of profiling technology for domestic cyber security experts.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.137-139
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• 2022

As cyber threats in the maritime industry increase due to the digital transformation, the needs for cyber security training for ship's crew and port engineers has increased. The training of seafarers is related to the IMO's STCW convention, so cyber security training also managed and certified, and it is necessary to develop a cybersecurity training system that reflects the characteristics of the OT systemof ships and ports. In this paper, with the goal of developing a training model based on the IMO cyber risk management guideline, developing a cyber security training model based on the characteristics of maritime industry threats, and improving the effectiveness of cyber security training using AR/VR and metaverse, A method for developing a system for nurturing cyber security experts is presented.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.1011-1020
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• 2021

Throughout the years, cybersecurity incidents related to the shipbuilding and maritime industries are occurring more frequently as the IT industry develops. Accordingly, expertise in the information protection industry is necessary, and effective education contents on information protection are needed for this purpose. Recently, there have been more and more cases of increasing user experience by applying Metaverse technology to the educational field. Therefore, this study analyzes the existing information protection education and training and the information protection education contents in the maritime industries and proposes four directions for content development (i.e., online education and seminars, cybersecurity threat learning of virtual ships, accident reproduction, and maritime cybersecurity exhibition operation).

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.65-72
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• 2020

With the development of information technology and the growth of the scale of system and network, cyber threats and crimes continue to increase. To cope with these threats, cybersecurity training based on actual attacks and defenses is required. However, cybersecurity training requires expert analysis and attack performance, which is inefficient in terms of cost and time. In this paper, we propose a cyber attack simulator that automatically executes attack techniques. This simulator generates attack scenarios by combining attack techniques modeled to be implemented and executes the attack by sequentially executing the derived scenarios. In order to verify the effectiveness of the proposed attack simulator, we experimented by setting an example attack goal and scenarios in a real environment. The attack simulator successfully performed five attack techniques to gain administrator privileges.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.48-60
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• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.501-511
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• 2018

The purpose of this study is to develop the performance evaluation indicator of information security training program for developing the next generation of top security leaders. Through literature review and focus group interview, we derived the performance areas and indicators based on the logic model. We conducted AHP(Analytic Hierarchy Process) questionnaire to calculate the weight of the derived indicators, and developed the performance indicator based on the survey results. Performance indicators were composed of 18 indicators in four main categories.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.19-24
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• 2022

The main purpose of the study is to analyze the current state of the organization's information security system in the context of COVID-19 on the basis of public-private partnership. The development of public-private interaction in information security is one of the priorities of the state policy of many estates. Among the priorities of public-private partnership in cybersecurity and information security, there is an expansion of interaction between government agencies and private scientific institutions, public associations and volunteer organizations, including in training, as well as increasing the digital literacy of citizens and the security culture in cyberspace. As a result of the study, the foundations of the organization's information security system in the context of COVID 19 were formed on the basis of public-private partnership.

• ETRI Journal
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• v.45 no.4
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• pp.713-723
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• 2023

Game bots are illegal programs that facilitate account growth and goods acquisition through continuous and automatic play. Early detection is required to minimize the damage caused by evolving game bots. In this study, we propose a game bot detection method based on action time intervals (ATIs). We observe the actions of the bots in a game and identify the most frequently occurring actions. We extract the frequency, ATI average, and ATI standard deviation for each identified action, which is to used as machine learning features. Furthermore, we measure the performance using actual logs of the Aion game to verify the validity of the proposed method. The accuracy and precision of the proposed method are 97% and 100%, respectively. Results show that the game bots can be detected early because the proposed method performs well using only data from a single day, which shows similar performance with those proposed in a previous study using the same dataset. The detection performance of the model is maintained even after 2 months of training without any revision process.