• Information Systems Review
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• v.18 no.3
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• pp.137-153
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• 2016

Organizations depend on smart working environments, such as mobile networks. This development motivates companies to focus on information security. Information leakage negatively affects companies. To address this issue, management and information security researchers focus on compliance of employees with information security policies. Countermeasures in information security are known antecedents of intention to comply information security policies. Despite the importance of this topic, research on the antecedents of information security countermeasures is scarce. The present study proposes information security intelligence as an antecedent of information security countermeasures. Information security intelligence adapted the concept of safety intelligence provided by Kirwan (2008). Information security intelligence consists of problem solving skills, social skills, and information security knowledge related to information security. Results show that problem solving skills and information security knowledge have positive effects on the awareness of employees of information security countermeasures.

• Convergence Security Journal
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• v.15 no.5
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• pp.71-76
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• 2015

Financial Intelligence Unit Law doesn't include investigation on important cases that could influence the security and existence of the nation that are the core jobs of national intelligence agency. So the agency has a difficulty to investigate the international crime of North Korea and other security incidents. It is also difficult to catch an international crime organization working in Korea. It also produces problems such as difficulty in investigating the illegal leak of strategic materials and investigating people related to illegal funding to international terrorism. So it is urgently needed to revise Financial Intelligence Law as soon as possible. Foreign intelligence agencies use the information of financial intelligence unit in many different ways. National Security Agency of China and Australian Security Intelligence Organization freely use the information of financial intelligence unit based on their own laws and systems. Central Intelligence Agency and Federal Bureau of Investigation of USA and Secret Intelligence Service and Security Service of Britain request financial intelligence units to supply them with the information of financial intelligence unit. But the national intelligence agency of Korea isn't able to approach to FIU and can't share the FIU information with foreign intelligence agencies. To solve the problem, they should revise Financial Intelligence Unit Law so that national intelligence agency can receive or request information from Korean Financial Intelligence Unit.

• Korean Security Journal
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• no.58
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• pp.107-135
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• 2019

The purpose of this paper is to compare and analyze the similarities and differences between the security environment, information organization and information activities of Korea and the United States. The comparison will provide insight into Korea and other national intelligence agencies, as well as methodological advances in information research, by providing insight into the overall information and a broad understanding As the history, culture and national power of Korea and the U.S. are different, the organization and activities of intelligence agencies are also different. First of all, in terms of environment, the U.S. carries out intelligence activities for national interest and security in a wide range of areas ranging from North American continental countries to South America, the Middle East, Asia and Asia, while South Korea's intelligence activities are mainly aimed at North Korea and neighboring countries around the Korean Peninsula. In terms of information organization, U.S. intelligence agencies are separate, whereas domestic and foreign intelligence agencies are separate, whereas Korean intelligence agencies are a type of integrated intelligence agency that combines information and investigation, unlike the U.S. In the U.S., the U.S. also operates as an intelligence community, and there are many flexible organizations such as non-tier organizations and centers. Intelligence activities by U.S. intelligence agencies are mainly focused on analysis and overseas processing activities, while Korean intelligence agencies still account for a large portion of domestic information activities. Despite these differences, Korea's intelligence agency was created by imitating U.S. intelligence agencies, and thus has similar aspects in terms of evaluation of security, organization and activities. However, this similarity is shared by all intelligence agencies, so the article will focus on analyzing differences. Finally, for the development of Korean intelligence agencies, the establishment of an intelligence community and efficient control of the National Assembly will be proposed.

• Smart Media Journal
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• v.13 no.3
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• pp.45-52
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• 2024

In today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1563-1583
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• 2018

With the deregulation of electronic finance, FinTech has been revitalized. The discussion on artificial intelligence is active in the financial industry. However, there is a problem of increasing security threats behind new technologies. Security vulnerabilities have increased because we are more connected than before, and the channels and entities of the financial industry have diversified. Although there are technical and policy discussions on security, the essence of all discussions is human. Fundamentals of finance are trust and security, and attention to human factors is important. This study presents the role of human and artificial intelligence for financial security, respectively. Furthermore, this derives a collaborative model in which human and artificial intelligence complement each other's limitations. To support this, it first discusses the development of finance and IT, AI, human factors, and financial security threats. This study suggests that the security threats will intensify in the era of new technology, but it can overcome them by using machinery and technology.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.13-27
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• 2023

In this study, we tried to identify research trends on artificial intelligence security focusing on the United States, United Kingdom, and South Korea. In Elsevier's Scopus We collected 4,983 papers related to artificial intelligence security published from 2018 to 2022 and by using the abstracts of the collected papers, Keyword frequency and centrality analysis were conducted. By calculating keyword frequency, keywords with high frequency of appearance were identified and through the centrality analysis, central research keywords were identified by country. Through the analysis results, research related to artificial intelligence, machine learning, Internet of Things, and cybersecurity in each country was conducted as the most central and highly mediating research. The implication for Korea is that research related to cybersecurity, privacy, and anomaly detection has lower centralities compared to the United States and research related to big data has lower centralities compared to United Kingdom. Therefore, various researches that intensively apply artificial intelligence technology to these fields are needed.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.108-116
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• 2019

In this paper, we propose a method to apply artificial intelligence technology efficiently to integrated security control technology. In other words, by applying machine learning learning to artificial intelligence based on big data collected in integrated security control system, cyber attacks are detected and appropriately responded. As technology develops, many large capacity Is limited to analyzing individual logs. The analysis method should also be applied to the integrated security control more quickly because it needs to correlate the logs of various heterogeneous security devices rather than one log. We have newly proposed an integrated security service model based on artificial intelligence, which analyzes and responds to these behaviors gradually evolves and matures through effective learning methods. We sought a solution to the key problems expected in the proposed model. And we developed a learning method based on normal behavior based learning model to strengthen the response ability against unidentified abnormal behavior threat. In addition, future research directions for security management that can efficiently support analysis and correspondence of security personnel through proposed security service model are suggested.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.53-58
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• 2021

There is little research on actual business activities in the field of security control. Therefore, in this paper, we intend to present a practical research methodology that can contribute to the calculation of the size of the appropriate input personnel through the modeling of the threat information detection response time of the security control and to analyze the effectiveness of the latest security solutions. The total threat information detection response time performed by the security control center is defined as TIDRT (Total Intelligence Detection & Response Time). The total threat information detection response time (TIDRT) is composed of the sum of the internal intelligence detection & response time (IIDRT) and the external intelligence detection & response time (EIDRT). The internal threat information detection response time (IIDRT) can be calculated as the sum of the five steps required. The ultimate goal of this study is to model the major business activities of the security control center with an equation to calculate the cyber threat information detection response time calculation formula of the security control center. In Chapter 2, previous studies are examined, and in Chapter 3, the calculation formula of the total threat information detection response time is modeled. Chapter 4 concludes with a conclusion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.260-280
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• 2020

Cyber-attacks emerge in a more intelligent way, and various security technologies are applied to respond to such attacks. Still, more and more people agree that individual response to each intelligent infringement attack has a fundamental limit. Accordingly, the cyber threat intelligence analysis technology is drawing attention in analyzing the attacker group, interpreting the attack trend, and obtaining decision making information by collecting a large quantity of cyber-attack information and performing relation analysis. In this study, we proposed relation analysis factors and developed a system for establishing cyber threat intelligence, based on malicious code as a key means of cyber-attacks. As a result of collecting more than 36 million kinds of infringement information and conducting relation analysis, various implications that cannot be obtained by simple searches were derived. We expect actionable intelligence to be established in the true sense of the word if relation analysis logic is developed later.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4625-4647
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• 2020

The Generative Adversarial Networks, as one of the most creative deep learning models in recent years, has achieved great success in computer vision and natural language processing. It uses the game theory to generate the best sample in generator and discriminator. Recently, many deep learning models have been applied to the security field. Along with the idea of "generative" and "adversarial", researchers are trying to apply Generative Adversarial Networks to the security field. This paper presents the development of Generative Adversarial Networks. We review traditional generation models and typical Generative Adversarial Networks models, analyze the application of their models in natural language processing and computer vision. To emphasize that Generative Adversarial Networks models are feasible to be used in security, we separately review the contributions that their defenses in information security, cyber security and artificial intelligence security. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.