• Convergence Security Journal
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• v.8 no.1
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• pp.19-26
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• 2008

In wireless sensor networks, an adversary can launch the wormhole attacks, where a malicious node captures packets at one location and tunnels them to a colluding node, which retransmits them locally. The wormhole attacks are very dangerous against routing protocols since she might launch these attacks during neighbor discovery phase. A strategic placement of a wormhole can result in a significant breakdown in communication across the network. This paper presents a compromise-resilient tunneled packet filtering method for sensor networks. The proposed method can detect a tunneled message with hop count alteration by a comparison between the hop count of the message and one of the encrypted hop counts attached in the message. Since the proposed method limits the amount of security information assigned to each node, the impact of wormhole attacks using compromised nodes can be reduced.

• Journal of the Korea Society of Computer and Information
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• v.15 no.6
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• pp.65-72
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• 2010

In ad-hoc network, the technique to efficiently consume the limited amounts of energy is an important issue since the wireless terminal node is operated on batteries as their energy resource. In order to extend the system lifetime, through a balanced energy consumption, we must delay the situation in which a particular terminal node's energy is depleted and results in system disconnection. Also, the link, which has low reliability due to the mobility of the node, should be avoided considering the key element when setting up the route. The proposed CMLR method in this paper enables to increase the efficiency of energy consumption with a new cost function considering the residue energy of node, error rate of link, and transmission energy consumption. This method is extending the network lifetime and increasing the energy efficiency by compromising the value between the minimization of the transmission energy consumption and maximization of the node's lifetime. Through the simulations the proposed CMLR algorithm was verified by showing better performance over the conventional methods in terms of network lifetime and path efficiency.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.75-86
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• 2014

Demand for the femtocell is largely credited to the surge in a more always best connected communication conscious public. 3GPP defines new architecture and security requirement for Release 9 to deal with femtocell, Home eNode B referred as HeNB. In this paper, we analyze the HeNB security with respect to mutual authentication, access control, and secure key agreement. Our analysis pointed out that a number of security vulnerabilities have still not been addressed and solved by 3GPP technical specification. These include eavesdropping, man-in-the-middle attack, compromising subscriber access list, and masquerading as valid HeNB. To the best of our knowledge, any related research studying HeNB security was not published before. Towards this end, this paper proposes an improved authentication and key agreement mechanism for HeNB which adopts proxy-signature and proxy-signed proxy-signature. Through our elaborate analysis, we conclude that the proposed not only prevents the various security threats but also accomplishes minimum distance from use-tolerable authentication delay.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.873-876
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• 2005

무선 센서 네트워크 (Wireless Sensor Network)에서 기존에 존재하는 대부분의 보안 프로토콜들은 대칭적인 공유키(symmetric pairwise key) 설정에 기반하고 있다. 그러나 이러한 프로토콜들은 노드 전복 (node compromising), 그리고 과중한 트래픽의 문제점을 안고 있다. 더욱이, 대칭키 방법을 이용한 브로드캐스트 메시지 인증은 자원이 제약된 센서네트워크에서 적용하기에는 너무 복잡하다. 본 논문은 공개키를 이용한 공유키(Pairwise Key) 설정에 기반한 보안 프로토콜들을 제안한다. 특히 경량성을 위하여 타원 곡선 암호 (Ellptic Curve Cryptography)를 채택하였다. 제안 프로토콜은 공유키 설정과 브로드캐스트 메시지 인증을 위하여 각각 Elliptic Curve Diffie-Hellman (ECDH)과 Elliptic Curve Digital Signature Algorithm (ECDSA)를 이용한다. 더욱이, 분산된 rekeying 메커니즘 (decentralized rekeying mechanism)을 도입함으로써 TinySec 의 성능을 향상시킨다.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.177-180
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• 2008

There are many application areas of sensor networks, such as surveillance, hospital monitoring, and home network. These are dependent on the secure operation of networks, and will have serious outcome if the networks is injured. An adversary can inject false data into the network through the compromising node. Ye et al. proposed a statistical en-route filtering scheme (SEF) to detect such false data during forwarding process. In this scheme, it is important that the choice of the threshold value since it trades off security and overhead. This paper presents an adaptive threshold value determining method in the SEF using fuzzy logic. The fuzzy logic determines a security distance value by considering the situation of the network. The Sensor network is divided into several areas by the security distance value, it can each area to uses the different threshold value. The fuzzy based threshold value can reduce the energy consumption in transmitting.

• ETRI Journal
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• v.45 no.2
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• pp.304-317
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• 2023

With the rapid evolution of quantum computing, digital quantum simulations are essential for quantum algorithm verification, quantum error analysis, and new quantum applications. However, the exponential increase in memory overhead and operation time is challenging issues that have not been solved for years. We propose a novel approach that provides more qubits and faster quantum operations with smaller memory than before. Our method selectively tracks realized quantum states using a reduced quantum state representation scheme instead of loading the entire quantum states into memory. This method dramatically reduces memory space ensuring fast quantum computations without compromising the global quantum states. Furthermore, our empirical evaluation reveals that our proposed idea outperforms traditional methods for various algorithms. We verified that the Grover algorithm supports up to 55 qubits and the surface code algorithm supports up to 85 qubits in 512 GB memory on a single computational node, which is against the previous studies that support only between 35 qubits and 49 qubits.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5385-5389
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• 2012

Introduction: Sentinel lymph node biopsy (SLNB) is a precise procedure for lymphatic staging in early breast cancer. In a valid SLNB procedure, axillary lymph node dissection (ALND) can be omitted in nodenegative cases without compromising patient safety. In this study, detection rate, accuracy and false negative rate of SLNB for breast cancer was evaluated in a setting with simple modified conventional pathology facilities without any serial sectioning or immunohistochemistry. Material and Medthod: Patients with confirmed breast cancer were enrolled in the study. SLNB and ALND were performed in all cases. Lymph node metastasis was evaluated in SLN and in nodes removed by ALND to determine the false negative rate. Pathologic assessment was carried out only by modified conventional technique with only 3 sections. Detection rate was determined either by lymphoscintigraphy or during surgery. Results: 78 patients with 79 breast units were evaluated. SLN was detected in 75 of 79 cases (95%) in lymphoscintigraphy and 76 of 79 cases (96%) during surgery. SLN metastases was detected in 30 of 75 (40%) cases either in SLNB and ALND groups. Accuracy of SLNB method for detecting LN metastases was 92%. False negative rate was 3 of 30 of positive cases: 10%. In 7 of 10 cases with axillary lymphadenopathy, LN metastastates was detected. Conclusion: SLNB is recommended for patients with various tumor sizes without palpable lymph nodes. In modified conventional pathologic examination of SLNs, at least macrometastases and some micrometastases could be detected similar to ALND. Consequently, ALND could be omitted in node-negative cases with removal of all palpable LNs. We conclude that SLNB, as one of the most important developments in breast cancer surgery, could be expanded even in areas without sophisticated pathology facilities.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.41-52
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• 2023

The purpose of this study is to review the proposed solutions to the Blockchain trilemma put forward by various research scholars and to draw conclusions by comparing the findings of each study. We found that the models so far developed either compromise scalability, decentralization, or security. The first model compromises decentralization. By partially centralizing the network, transaction processing speed can be improved, but security strength is weakened. Examples of this include Algorand and EOS. Because Algorand randomly selects the node that decides the consensus, the security of Algorand is better than EOS, wherein a designated selector decides. The second model recognizes that scalability causes a delay in speed when transactions are included in a block, reducing the system's efficiency. Compromising scalability makes it possible to increase decentralization. Representative examples include Bitcoin and Ethereum. Bitcoin is more vital than Ethereum in terms of security, but in terms of scalability, Ethereum is superior to Bitcoin. In the third model, information is stored and managed through various procedures at the expense of security. The application case is to weaken security by applying a layer 1 or 2 solution that stores and reroutes information. The expected effect of this study is to provide a new perspective on the trilemma debate and to stimulate interest in continued research into the problem.

• Smart Structures and Systems
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• v.14 no.1
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• pp.39-54
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• 2014

Node layout optimization of structural wireless systems is investigated as a means to prolong the network lifetime without, if possible, compromising information quality of the measurement data. The trade-off between these antagonistic objectives is studied within a multi-objective layout optimization framework. A Genetic Algorithm is adopted to obtain a set of Pareto-optimal solutions from which the end user can select the final layout. The information quality of the measurement data collected from a heterogeneous WSN is quantified from the placement quality indicators of strain and acceleration sensors. The network lifetime or equivalently the network energy consumption is estimated through WSN simulation that provides realistic results by capturing the dynamics of the wireless communication protocols. A layout optimization study of a monitoring system on the Great Belt Bridge is conducted to evaluate the proposed approach. The placement quality of strain gauges and accelerometers is obtained as a ratio of the Modal Clarity Index and Mode Shape Expansion values that are computed from a Finite Element model of the monitored bridge. To estimate the energy consumption of the WSN platform in a realistic scenario, we use a discrete-event simulator with stochastic communication models. Finally, we compare the optimization results with those obtained in a previous work where the network energy consumption is obtained via deterministic communication models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.