• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.187-192
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• 2022

Even the announcing date for the staring date of "With Corona" has been decided, still many people have not completed vaccination, the most important condition for starting the With Corona, because of concerns for its side effects. In addition, although the economy may can be recovered by the With Corona, but the number of infected people may can be surged. In this paper, in order to awaken the people for the awareness of Corona 19 in advance of the With Corona, the Corona 19 is predicted through a non-linear probability process. Here, among the deep learning RNN, BI-LSTM, which is a bidirectional LSTM, and GRU, gates decreased than LSTM have been used. And this has been compared and analyzed through train set, test set, loss function, residual analysis, normal distribution, and autocorrelation, and compared and predicted for which has a better performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.199-201
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• 2021

The Internet of Things (IoT) is a technology that can organically connect people and things without time and space constraints by using communication network technology and sensors, and transmit and receive data in real time. The IoT used in all industrial fields has limitations in terms of storage allocation, such as device size, memory capacity, and data transmission performance, so it is important to manage power consumption to effectively utilize the limited battery capacity. In the prior research, there is a problem in that security is deteriorated instead of improving power efficiency by lightening the security algorithm of the encryption module. In this study, we proposes a low-power security architecture that can utilize high-performance security algorithms in the IoT environment. This can provide high security and power efficiency by using relatively complex security modules in low-power environments by executing security modules only when threat detection is required based on inspection results.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.12
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• pp.357-362
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• 2023

Bridges are important transportation infrastructure, but they are subject to damage and cracking due to various environmental factors and constant traffic loads, which accelerate their aging. With many bridges now older than their original construction, there is a need for systems to ensure safety and diagnose deterioration. Bridges are already utilizing structural health monitoring (SHM) technology to monitor the condition of bridges in real time or periodically. Along with this technology, the development of intelligent bridge monitoring technology utilizing artificial intelligence and Internet of Things technology is underway. In this paper, we study an edge system technique for predicting bridge safety using fast Fourier transform and dimensionality reduction algorithm for maintenance of aging bridges. In particular, unlike previous studies, we investigate whether it is possible to form a dataset using sensor data collected from actual bridges and check the safety of bridges.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.113-120
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• 2024

With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.131-135
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• 2024

In the MQTT protocol, if the retained flag of a message published by a publisher is set, the message is stored in the broker as a retained message. When a new subscriber performs a subscribe, the broker immediately sends the retained message. This allows the new subscriber to perform updates on the current state via the retained message without waiting for new messages from the publisher. However, sending retained messages can become a traffic overhead if new messages are frequently published by the publisher. This situation could be considered an overhead when new subscribers frequently subscribe. Therefore, in this paper, we propose a retained message delivery scheme by considering the characteristics of the published messages. We model the delivery and waiting actions to new subscribers from the perspective of the broker using reinforcement learning, and determine the optimal policy through Q learning algorithm. Through performance analysis, we confirm that the proposed method shows improved performance compared to existing methods.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.121-130
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• 2024

In this paper, we propose an installation approach for Naval Combat Management System(CMS) software that identifies potential data anomalies during installation. With the popularization of wireless communication methods, such as Low Earth Orbit(LEO) satellite communications, various utilization methods using wireless networks are being discussed in CMS. One of these methods includes the use of wireless network communications for installation, which is expected to enhance the real-time performance of the CMS. However, wireless networks are relatively more vulnerable to security threats compared to wired networks, necessitating additional security measures. This paper presents a method where files are transmitted to multiple nodes using encryption, and after the installation of the files, a validity check is performed to determine if there has been any tampering or alteration during transmission, ensuring proper installation. The feasibility of applying the proposed method to Naval Combat Systems is demonstrated by evaluating transmission performance, security, and stability, and based on these evaluations, results sufficient for application to CMS have been derived.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.950-953
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• 2007

무선 인프라와 이동성 지원 기술의 발달로 한 장소에서 머물던 무선 인터넷 서비스를 이동 중에도 받을 수 있게 되었다. 이러한 환경의 변화로 인해 모바일 기기의 이동성에 대한 관심은 더욱 더 커지고 있으며, 이동성뿐만 아니라 유선과 마찬가지로 끊김 없는 서비스를 받고자 하는 요구도 높아지고 있다. 모바일 노드의 이동성을 지원하기 위해서 기존 네트워크 계층의 Mobile IP 와는 달리 전송 계층에서 동작하는 mSCTP (mobile Stream Control Transmission Protocol)가 등장하였다. mSCTP 는 기존 SCTP 의 멀티호밍과 동적으로 IP 주소를 변경할 수 있는 DAR (Dynamic Address Resolution) 시그널을 이용하여 모바일 노드의 핸드오프를 지원하고 있다. 하지만, IP 주소의 추가, 변경 및 삭제에 대한 각 시그널의 전송 시기에 대한 정의가 없어 전송 시기를 결정하는 메커니즘이 필요하다. 따라서 본 논문에서는 퍼지 IF-THEN 룰을 적용한 퍼지 모델을 이용하여 이러한 문제점을 해결하고자 한다. 모바일 노드가 이동하게 될 새로운 네트워크의 신호 세기와 현재 네트워크 신호와의 신호비를 퍼지 모델에 입력하고 그 결과 값을 통해 시그널 전송 시기를 판단한다. 모바일 노드는 핸드오프 시기를 적절히 판단 할 수 있기 때문에 잘못된 핸드오프로 인한 세션의 단절을 줄일 수 있고, 기존에 발생하던 핸드오프 지연 시간을 줄일 수 있어 이동 중에도 손실 없는 서비스를 제공 받을 수 있게 된다.

• Convergence Security Journal
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• v.24 no.2
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• pp.71-78
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• 2024

As the rapid development of quantum computing compromises current public key encryption methods, the National Institute of Standards and Technology (NIST) in the United States has initiated the Post-Quantum Cryptography(PQC) project to develop new encryption standards that can withstand quantum computer attacks. This project involves reviewing and evaluating various cryptographic algorithms proposed by researchers worldwide. The initially selected quantum-resistant cryptographic algorithms were developed based on lattices and hash functions. Currently, algorithms offering diverse technical approaches, such as BIKE, Classic McEliece, and HQC, are under review in the fourth round. CRYSTALS-KYBER, CRYSTALS-Dilithium, FALCON, and SPHINCS+ were selected for standardization in the third round. In 2024, a final decision will be made regarding the algorithms selected in the fourth round and those currently under evaluation. Strengthening the security of public key cryptosystems in preparation for the quantum computing era is a crucial step expected to have a significant impact on protecting future digital communication systems from threats. This paper analyzes the security and efficiency of quantum-resistant cryptographic algorithms, presenting trends in this field.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.226-234
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• 2024

The frequent handover problem and playing ping-pong effects in 5G (5th Generation) ultra-dense networking cannot be effectively resolved by the conventional handover decision methods, which rely on the handover thresholds and measurement reports. For instance, millimetre-wave LANs, broadband remote association techniques, and 5G/6G organizations are instances of group of people yet to come frameworks that request greater security, lower idleness, and dependable principles and correspondence limit. One of the critical parts of 5G and 6G innovation is believed to be successful blockage the board. With further developed help quality, it empowers administrator to run many systems administration recreations on a solitary association. To guarantee load adjusting, forestall network cut disappointment, and give substitute cuts in case of blockage or cut frustration, a modern pursuing choices framework to deal with showing up network information is require. Our goal is to balance the strain on BSs while optimizing the value of the information that is transferred from satellites to BSs. Nevertheless, due to their irregular flight characteristic, some satellites frequently cannot establish a connection with Base Stations (BSs), which further complicates the joint satellite-BS connection and channel allocation. SF redistribution techniques based on Deep Reinforcement Learning (DRL) have been devised, taking into account the randomness of the data received by the terminal. In order to predict the best capacity improvements in the wireless instruments of 5G and 6G IoT networks, a hybrid algorithm for deep learning is being used in this study. To control the level of congestion within a 5G/6G network, the suggested approach is put into effect to a training set. With 0.933 accuracy and 0.067 miss rate, the suggested method produced encouraging results.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.27-33
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• 2024

As of 2023, the number of accident victims in the domestic construction industry is 26,829, ranking second only to other businesses (service industries). The accident types of casualties in all industries were falls (29,229 people), followed by falls (14,357 people). Based on the above data, this study attaches sensors to hard hats and insoles to predict fall accidents that frequently occur at construction sites, and proposes smart safety equipment that applies a random forest algorithm based on the data collected through this. The random forest model can determine fall accidents in real time with high accuracy by generating multiple decision trees and combining the predictions of each tree. This model classifies whether a worker has had a fall accident and the type of behavior through data collected from the MPU-6050 sensor attached to the hard hat. Fall accidents that are primarily determined from hard hats are secondarily predicted through sensors attached to the insole, thereby increasing prediction accuracy. It is expected that this will enable rapid response in the event of an accident, thereby reducing worker deaths and accidents.