• Convergence Security Journal
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• v.8 no.4
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• pp.173-181
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• 2008

Recently, Worm epidemic models have been developed in response to the cyber threats posed by worms in order to analyze their propagation and predict their spread. Some of the most important ones involve mathematical model techniques such as Epidemic(SI), KM (Kermack-MeKendrick), Two-Factor and AAWP(Analytical Active Worm Propagation). However, most models have several inherent limitations. For instance, they target worms that employ random scanning in the network such as CodeRed worm and it was able to be applied to the specified threats. Therefore, we propose the probabilistic of worm propagation based on the Markov Chain, which can be applied to cyber threats such as Mass SQL Injection worm. Using the proposed method in this paper, we can predict the occurrence probability and occurrence frequency for each threats in the entire system.

• Journal of the Korea Convergence Society
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• v.8 no.6
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• pp.17-27
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• 2017

Recently, as cyber attacks have been activated, many such attacks have come into contact with various media. Research on security engineering and reverse engineering is active, but there is a lack of research that integrates them and applies attack systems through cost effective attack engineering. In this paper, security - enhanced information systems are developed by security engineering and reverse engineering is used to identify vulnerabilities. Using this vulnerability, we compare and analyze lifecycle models that construct or remodel attack system through attack engineering, and specify structure and behavior of each system, and propose more effective modeling. In addition, we extend the existing models and tools to propose graphical attack specification models that specify attack methods and scenarios in terms of models such as functional, static, and dynamic.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.598-599
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• 2016

Today, embedded, mobile, and cyber-physical systems are ubiquitous and used in many applications, from industrial control systems, modern vehicles, to critical infrastructure. Current trends and initiatives, such as "Industry 4.0" and Internet of Things (IoT), promise innovative business models and novel user experiences through strong connectivity and effective use of next generation of embedded devices. We survey an introduction to Industrial IoT systems, the related security and privacy challenges, and an outlook on possible solutions towards a holistic security framework for Industrial IoT systems in this paper.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.73-85
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• 2022

In the era of the 4th industrial revolution, where automation and connectivity are maximized with artificial intelligence, the importance of data collection and utilization for model update is increasing. In order to create a model using artificial intelligence technology, it is usually necessary to gather data in one place so that it can be updated, but this can infringe users' privacy. In this paper, we introduce federated learning, a distributed machine learning method that can update models in cooperation without directly sharing distributed stored data, and introduce a study to optimize distributed consensus among participants without an existing server. In addition, we propose a pattern and group-based distributed consensus optimization algorithm that uses an algorithm for generating patterns and groups based on the Kirkman Triple System, and performs parallel updates and communication. This algorithm guarantees more privacy than the existing distributed consensus optimization algorithm and reduces the communication time until the model converges.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.25-36
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• 2023

Nuclear power plants have recognized the importance of nuclear cybersecurity. Based on regulatory guidelines and security-related standards issued by regulatory agencies around the world including IAEA, NRC, and KINAC, nuclear operating organizations and related systems manufacturing organizations, design companies, and regulatory agencies are considering methods to prepare for nuclear cybersecurity. Cryptographic algorithms have to be developed and applied in order to meet nuclear cybersecurity requirements. This paper presents methodologies for validating cryptographic algorithms that should be continuously applied at the critical control system of I&C in NPPs. Through the proposed schemes, validation programs are developed in the PLC, which is a critical system of a NPP's I&C, and the validation program is verified through simulation results. Since the development of a cryptographic algorithm validation program for critical digital systems of NPPs has not been carried out, the methodologies proposed in this paper could provide guidelines for Cryptographic Module Validation Modeling for Control Systems in NPPs. In particular, among several CMVP, specific testing techniques for ECB mode-based block ciphers are introduced with program codes and validation models.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1021-1031
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• 2023

State-sponsored cyber attacks are highly impactful because they are carried out to achieve pre-planned goals. As a defender, it is difficult to respond to them because of the large scale of the attack and the possibility that unknown vulnerabilities may be exploited. In addition, overreacting can reduce the availability of users and cause business disruption. Therefore, there is a need for a response policy that can effectively defend against attacks while ensuring user availability. To solve this problem, this paper proposes a method to collect the number of processes and sessions of defense assets in real time and use them for learning. Using this method to learn reinforcement learning-based policies on a cyber attack simulator, the attack duration based on 100 time-steps was reduced by 27.9 time-steps and 3.1 time-steps for two attacker models, respectively, and the number of "restore" actions that impede user availability during the defense process was also reduced, resulting in an overall better policy.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.123-140
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• 2022

Recently, research on solving problems that have introduced the concept of smart city in countries and companies around the world is in progress due to various urban problems. A smart city converges the city's ICT, connects all the city's components with a network, collects and delivers data, and consists of a supply chain composed of various IoT products and services. The increase in various cyber security threats and supply chain threats in smart cities is inevitable, in addition to establishing a framework such as supply chain security policy, authentication of each data provider and service according to data linkage and appropriate access control are required in a Zero-Trust point of view. To this end, a smart city security model has been developed for smart city security threats in Korea, but security requirements related to supply chain security and zero trust are insufficient. This paper examines overseas smart city security trends, presents international standard security requirements related to ISMS-P and supply chain security, as well as security requirements for applying zero trust related technologies to domestic smart city security models.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.143-153
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• 2022

With the development of networks and the increase in the number of network devices, the number of cyber attacks targeting them is also increasing. Since these cyber-attacks aim to steal important information and destroy systems, it is necessary to minimize social and economic damage through early detection and rapid response. Many studies using machine learning (ML) and artificial intelligence (AI) have been conducted, among which payload learning is one of the most intuitive and effective methods to detect malicious behavior. In this study, we propose a preprocessing method to maximize the performance of the model when learning the payload in term units. The proposed method constructs a high-quality learning data set by eliminating unnecessary noise (stopwords) and preserving important features in consideration of the machine language and natural language characteristics of the packet payload. Our method consists of three steps: Preserving significant special characters, Generating a stopword list, and Class label refinement. By processing packets of various and complex structures based on these three processes, it is possible to make high-quality training data that can be helpful to build high-performance ML/AI models for security monitoring. We prove the effectiveness of the proposed method by comparing the performance of the AI model to which the proposed method is applied and not. Forthermore, by evaluating the performance of the AI model applied proposed method in the real-world Security Operating Center (SOC) environment with live network traffic, we demonstrate the applicability of the our method to the real environment.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.181-188
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• 2014

Heterogeneous physical systems and computational devices are incorporated on a large-scale in a CPS (cyber-physical system) environment. Simulations can be useful for the reliable behaviors of CPSs. Time synchronization is one of major technical issues for the simulations. In the CPS, distributed systems control themselves by interacting with each other during runtime. When some simulation models have high complexity, wrong control commands as well as incorrect data can be exchanged due to the time error. We propose a time synchronization algorithm for the hybrid model that has characteristics of both continuous time systems and discrete event systems. In addition, we develop a CPS simulator based on our algorithm. For the verification of the algorithm and the execution of the simulator, we develop an example hybrid model and simulate considering user controls as well as interactions among the distributed systems.