• Journal of Korean Society of Coastal and Ocean Engineers
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• v.10 no.1
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• pp.1-9
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• 1998

Among the phenomena of water-wave transformation, the wave diffraction is prominent for waves insidc the harbor. It is important to study how accurately the diffraction can be resolved by the numerical model. Three parabolic mild-slope equations, i.e., simple, wide-ang1e, three-parameter parabolic equations, are compared in view of the diffraction of water-waves around a semi-infinite breakwater. To avoid reflections at lateral boundaries, we apply the perfect boundary condition of Dalrymple and Martin (1992) in case of simple parabolic equation. The numerical results for the case of a semi-infinite breakwater are compared with the analytical solution of Penney and Price (1952). All the results are very accurate when waves attack the breakwater normally. When waves attack the breakwater obliquely, however, the simple parabolic equation yields the worst solution and the three-parameter parabolic equation yields the most accurate solution.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.5
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• pp.35-45
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• 2011

The Salsa20/12 stream cipher selected for the final eSTREAM portfolio has a better performance than software implementation of AES using an 8-bit microprocessor with restricted memory space, In the theoretical approach, the evaluation of exploitable timing vulnerability was 'none' and the complexity of side-channel analysis was 'low', but there is no literature of the practical result of power analysis attack. Thus we propose the correlation power analysis attack method and prove the feasibility of our proposed method by practical experiments, We used an 8-bit RISC AVR microprocessor (ATmegal128L chip) to implement Salsa20/12 stream cipher without any countermeasures, and performed the experiments of power analysis based on Hamming weight model.

• Journal of IKEEE
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• v.25 no.2
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• pp.362-370
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• 2021

In this paper, we propose the Autonomous Stair-climbing system based on data from ToF sensors and IMU in developing stair-climbing robots to passive wheelchair users. Autonomous stair-climbing system are controlled by separating the timing of landing gear operation by location and utilizing state machines. To prove the theory, we construct and experiment with standard model stairs. Through an experiment to get the Attack angle, the average error of operating landing gear was 2.19% and the average error of the Attack angle was 2.78%, and the step division and status transition of the autonomous stair-climbing system were verified. As a result, the performance of the proposed techniques will reduce constraints of transportation handicapped.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1187-1192
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• 2020

The dronebot combat system is a representative model of the future battlefield in the 4th industrial revolution. In dronebot, unmanned reconnaissance tankrobot can minimize human damage and reduce cost with higher combat power than humans. However, since the battlefield environment is very complex such as obstacles and enemy situations, it is also necessary for the pilot to control the tankrobot. Tankrobot are robots with new ICT technology, capable of hacking attacks, and if there is an abnormality in control, it can pose a threat to manipulation and control. A Bluetooth sniffing attack was performed on the communication section of the tankrobot and the controller to introduce a vulnerability to Bluetooth, and a countermeasure using MAC address exposure prevention and communication section encryption was proposed as a security measure. This paper first presented the vulnerability of tankrobot to be operated in future military operations, and will be the basic data that can be used for defense dronebot units.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.15-26
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• 2022

Various subjects are carrying out cyberattacks using a variety of tactics and techniques. Additionally, cyberattacks for political and economic purposes are also being carried out by groups which is sponsored by its nation. To deal with cyberattacks, researchers used to classify the malware family and the subjects of the attack based on malware signature. Unfortunately, attackers can easily masquerade as other group. Also, as the attack varies with subject, techniques, and purpose, it is more effective for defenders to identify the attacker's purpose and goal to respond appropriately. The essential goal of cyberattacks is to threaten the information security of the target assets. Information security is achieved by preserving the confidentiality, integrity, and availability of the assets. In this paper, we relabel the attacker's goal based on MITRE ATT&CK® in the point of CIA triad as well as classifying cyber security reports to verify the labeling method. Experimental results show that the model classified the proposed CIA label with at most 80% probability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1310-1338
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• 2023

As Internet of Things (IoT) applications and devices rapidly grow, cyber-attacks on IoT networks/systems also have an increasing trend, thus increasing the threat to security and privacy. Botnet is one of the threats that dominate the attacks as it can easily compromise devices attached to an IoT networks/systems. The compromised devices will behave like the normal ones, thus it is difficult to recognize them. Several intelligent approaches have been introduced to improve the detection accuracy of this type of cyber-attack, including deep learning and machine learning techniques. Moreover, dimensionality reduction methods are implemented during the preprocessing stage. This research work proposes deep Autoencoder dimensionality reduction method combined with Artificial Neural Network (ANN) classifier as botnet detection system for IoT networks/systems. Experiments were carried out using 3- layer, 4-layer and 5-layer pre-processing data from the MedBIoT dataset. Experimental results show that using a 5-layer Autoencoder has better results, with details of accuracy value of 99.72%, Precision of 99.82%, Sensitivity of 99.82%, Specificity of 99.31%, and F1-score value of 99.82%. On the other hand, the 5-layer Autoencoder model succeeded in reducing the dataset size from 152 MB to 12.6 MB (equivalent to a reduction of 91.2%). Besides that, experiments on the N_BaIoT dataset also have a very high level of accuracy, up to 99.99%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.469-471
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• 2021

As wireless communication technology advances to ensure high accuracy and safety at high speeds, research and development of Visible Light Communication (VLC) technology has been accelerated as an alternative to traditional radio frequency (RF) technology. As the radio spectrum of RF communication becomes more congested and demand for bandwidth continues to increase, VLCs that can use unlicensed frequency band are proposed as a solution. However, VLC channels have broadcasting characteristics that make them easily exposed to eavesdropping and jamming attacks, and are vulnerable to MITM (Man-In-The-Middle) due to their line of sight (LOS) propagation characteristics. These attacks on VLC channels compromise the confidentiality, integrity, and availability of communications links and data, resulting in higher data retransmission rates, reducing throughput and increasing power consumption, resulting in lower data transmission efficiency. In this work, we model vulnerable VLC channels to analyze the impact of attacks and communications vulnerabilities by malicious jammers.

• Convergence Security Journal
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• v.22 no.2
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• pp.51-57
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• 2022

Recently, the concept of zero trust has been introduced, and it is necessary to strengthen the security elements required for the next-generation security control system. Also, the security paradigm in the era of the 4th industrial revolution is changing. Cloud computing and the cybersecurity problems caused by the dramatic changes in the work environment due to the corona 19 virus continue to occur. And at the same time, new cyber attack techniques are becoming more intelligent and advanced, so a future security control system is needed to strengthen security. Based on the core concept of doubting and trusting everything, Zero Trust Security increases security by monitoring all communications and allowing strict authentication and minimal access rights for access requesters. In this paper, we propose a security enhancement plan in the security control field through a zero trust security model that can understand the problems of the existing security control system and solve them.

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.121-128
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• 2019

Computing systems have various vulnerabilities to cyber attacks. In particular, various cyber attacks that are intelligent in the information society have caused serious social problems and economic losses. Traditional security systems are based on misuse-based technology, which requires the continuous updating of new attack patterns and the real-time analysis of vast amounts of data generated by numerous security devices in order to accurately detect. However, traditional security systems are unable to respond through detection and analysis in real time, which can delay the recognition of intrusions and cause a lot of damage. Therefore, there is a need for a new security system that can quickly detect, analyze, and predict the ever-increasing cyber security threats based on machine learning and big data analysis models. In this paper, we present a IDS model that combines machine learning and big data technology.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.451-461
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• 2024

Recently, various AI chatbots based on large language models have been released. Chatbots have the advantage of providing users with quick and easy information through interactive prompts, making them useful in various fields such as question answering, writing, and programming. However, a vulnerability in chatbots called "prompt injection attacks" has been proposed. This attack involves injecting instructions into the chatbot to violate predefined guidelines. Such attacks can be critical as they may lead to the leakage of confidential information within large language models or trigger other malicious activities. However, the vulnerability of Korean prompts has not been adequately validated. Therefore, in this paper, we aim to generate malicious Korean prompts and perform attacks on the popular chatbot to analyze their feasibility. To achieve this, we propose a system that automatically generates malicious Korean prompts by analyzing existing prompt injection attacks. Specifically, we focus on generating malicious prompts that induce harmful expressions from large language models and validate their effectiveness in practice.