• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.103-110
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• 2022

Botnets have been exploited for a variety of purposes, ranging from monetary demands to national threats, and are one of the most threatening types of attacks in the field of cybersecurity. Botnets emerged as a centralized structure in the early days and then evolved to a P2P structure. Bitcoin is the first online cryptocurrency based on blockchain technology announced by Satoshi Nakamoto in 2008 and is the most widely used cryptocurrency in the world. As the number of Bitcoin users increases, the size of Bitcoin network is also expanding. As a result, a botnet using the Bitcoin network as a C&C channel has emerged, and related research has been recently reported. In this study, we propose an encrypted botnet C&C communication mechanism and technique in the Bitcoin network and validate the proposed method by conducting performance evaluation through various experiments after building it on the Bitcoin testnet. By this research, we want to inform the possibility of botnet threats in the Bitcoin network to researchers.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.267-277
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• 2022

Due to the increasing proportion of cloud and remote working environments, various information security incidents are occurring. Insider threats have emerged as a major issue, with cases in which corporate insiders attempting to leak confidential data by accessing it remotely. In response, insider threat detection approaches based on machine learning have been developed. However, existing machine learning methods used to detect insider threats do not take biases and variances into account, which leads to limited performance. In this paper, boosting-type ensemble learning algorithms are applied to verify the performance of malicious insider detection, conduct a close analysis, and even consider the imbalance in datasets to determine the final result. Through experiments, we show that using ensemble learning achieves similar or higher accuracy to other existing malicious insider detection approaches while considering bias-variance tradeoff. The experimental results show that ensemble learning using bagging and boosting methods reached an accuracy of over 98%, which improves malicious insider detection performance by 5.62% compared to the average accuracy of single learning models used.

• Convergence Security Journal
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• v.23 no.5
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• pp.65-72
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• 2023

This paper proposes a method for detecting malicious domains considering human habitual characteristics by building a Deep Learning model based on LSTM (Long Short-Term Memory). DGA (Domain Generation Algorithm) malicious domains exploit human habitual errors, resulting in severe security threats. The objective is to swiftly and accurately respond to changes in malicious domains and their evasion techniques through typosquatting to minimize security threats. The LSTM-based Deep Learning model automatically analyzes and categorizes generated domains as malicious or benign based on malware-specific features. As a result of evaluating the model's performance based on ROC curve and AUC accuracy, it demonstrated 99.21% superior detection accuracy. Not only can this model detect malicious domains in real-time, but it also holds potential applications across various cyber security domains. This paper proposes and explores a novel approach aimed at safeguarding users and fostering a secure cyber environment against cyber attacks.

• Journal of Information Technology Services
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• v.23 no.1
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• pp.1-10
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• 2024

The IT environment is changing due to the acceleration of digital transformation in enterprises and organizations. This expansion of the digital space makes centralized cybersecurity controls more difficult. For this reason, cyberattacks are increasing in frequency and severity and are becoming more sophisticated, such as ransomware and digital supply chain attacks. Even in large organizations with numerous security personnel and systems, security incidents continue to occur due to unmanaged and unknown threats and vulnerabilities to IT assets. It's time to move beyond the current focus on detecting and responding to security threats to managing the full range of cyber risks. This requires the implementation of asset Inventory for comprehensive management by collecting and integrating all IT assets of the enterprise and organization in a wide range. IT Asset Management(ITAM) systems exist to identify and manage various assets from a financial and administrative perspective. However, the asset information managed in this way is not complete, and there are problems with duplication of data. Also, it is insufficient to update of data-set, including Network Infrastructure, Active Directory, Virtualization Management, and Cloud Platforms. In this study, we, the researcher group propose a new framework for automated 'Comprehensive IT Asset Management(CITAM)' required for security operations by designing a process to automatically collect asset data-set. Such as the Hostname, IP, MAC address, Serial, OS, installed software information, last seen time, those are already distributed and stored in operating IT security systems. CITAM framwork could classify them into unique device units through analysis processes in term of aggregation, normalization, deduplication, validation, and integration.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.7
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• pp.321-328
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• 2017

As the cyber threats become more sophisticated and intelligent, the cases of cyber-infringement accidents are rapidly increasing. As a result, awareness of the importance of cyber security professionals has led to many cyber security-related educational programs. These programs provided with education curriculum aimed because cyber security workforce and job-based cyber security education research are not properly done. In this study, we developed a new cyber security education curriculum that defines and reflects cyber security personnel and knowledge system. In this study is not composed solely of the education contents related to the defenses emphasized in the existing education curriculum, but developed education curriculum to train a professional and balanced cyber security manpower by adding education contents in the attack field.

• Convergence Security Journal
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• v.19 no.4
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• pp.67-75
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• 2019

Recently, the Ministry of National Defense has included embedded software for weapon systems as targets for the Defense cyber security. The Concept has been extended and evolved from the cyber security area that was previously limited to the information domain. The software is becoming increasingly important in weapon systems, and it is clear that they are subject to cyber threats. Therefore, We would like to suggest a improvement direction by diagnosing problems in terms of cyber security of the weapon systems for the life cycle. In order to improve cyber security of weapon systems, comprehensive policy including the weapon embedded software management should be established and the involved stakeholder should be participated in the activities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.358-361
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• 2016

The North and South Korea for the peaceful reunification of the Republic of Korea, to lead the transformation and reform, and to complement the policy making and negotiations, there is a need for cyber security policy to practice. This paper explores the definition and overseas cyber terrorism and cyber warfare correspondence, correspondence between the versions of the technology between versions. Analysis of cyber security activities in the North and South confrontation, and research the cyber security policy against the unification. In this study, we compared the unification to build and operate a secure cyberspace from cyber threats and cyber security policy suggestions for ways of rational and legal.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.1-10
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• 2021

The Internet of things (IoT) is the main advancement in data processing and communication technologies. In IoT, intelligent devices play an exciting role in wireless communication. Although, sensor nodes are low-cost devices for communication and data gathering. However, sensor nodes are more vulnerable to different security threats because these nodes have continuous access to the internet. Therefore, the multiparty security credential-based key generation mechanism provides effective security against several attacks. The key generation-based methods are implemented at sensor nodes, edge nodes, and also at server nodes for secure communication. The main challenging issue in a collaborative key generation scheme is the extensive multiplication. When the number of parties increased the multiplications are more complex. Thus, the computational cost of batch key and multiparty key-based schemes is high. This paper presents a Secure Multipart Key Distribution scheme (SMKD) that provides secure communication among the nodes by generating a multiparty secure key for communication. In this paper, we provide node authentication and session key generation mechanism among mobile nodes, head nodes, and trusted servers. We analyzed the achievements of the SMKD scheme against SPPDA, PPDAS, and PFDA schemes. Thus, the simulation environment is established by employing an NS 2. Simulation results prove that the performance of SMKD is better in terms of communication cost, computational cost, and energy consumption.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.1-11
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• 2024

Payment systems are evolving, and this study examines how blockchain and AI improve online transactional security and service quality. The study examines micro and macro payment systems, compares online, and offline methods all over the world. The study also examines how blockchain and AI affect payment system security, privacy, and efficiency globally and rapidly digitizing economy. Digital payment methods are growing all over the world with high literacy and digital engagement, but they face challenges. The research highlights cybersecurity threats and the need to balance user convenience and security. It suggests blockchain and AI improve online payment services, supporting the policies for different countries. In this extensive research survey, we compare and evaluate the strengths and weaknesses of various payment systems, their practicality, and their robustness. This study also examines how technological innovations and payment systems interact to reveal how blockchain and AI could transform the financial sector. It seeks to understand how technology-enhancing service quality can boost customer satisfaction and financial stability in the digital age. The findings should help policymakers, financial institutions, and technology developers optimize online payment systems for a more secure and efficient digital economy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.465-479
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• 2020

Regardless of the domestic and foreign governments/companies, SOC (Security Operation Center) has operated 24 hours a day for the entire year to ensure the security for their IT infrastructures. However, almost all SOCs have a critical limitation by nature, caused from heavily depending on the manual analysis of human agents with the text-based monitoring architecture. Even though, in order to overcome the drawback, technologies for a comprehensive visualization against complex cyber threats have been studying, most of them are inappropriate for the security monitoring in large-scale networks. In this paper, to solve the problem, we propose a novel visual approach for intuitive threats monitoring b detecting suspicious IP address, which is an ultimate challenge in cyber security monitoring. The approach particularly makes it possible to detect, trace and analysis of suspicious IPs statistically in real-time manner. As a result, the system implemented by the proposed method is suitably applied and utilized to the real-would environment. Moreover, the usability of the approach is verified by successful detecting and analyzing various attack IPs.