• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5639-5653
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• 2016

Security models for key exchange protocols have been researched for years, however, lots of them only focus on what secret can be compromised but they do not differentiate the timing of secrets compromise, such as the extended Canetti-Krawczyk (eCK) model. In this paper, we propose a new security model for key exchange protocols which can not only consider what keys can be compromised as well as when they are compromised. The proposed security model is important to the security proof of the key exchange protocols with forward secrecy (either weak forward secrecy (wFS) or strong forward secrecy (sFS)). In addition, a new kind of key compromise impersonation (KCI) attacks which is called strong key compromise impersonation (sKCI) attack is proposed. Finally, we provide a new one-round key exchange protocol called mOT+ based on mOT protocol. The security of the mOT+ is given in the new model. It can provide the properties of sKCI-resilience and sFS and it is secure even if the ephemeral key reveal query is considered.

• Annual Conference of KIPS
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• 2001.10a
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• pp.175-178
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• 2001

동영상 정보는 압축율을 높이기 위해 서로 연관성이 깊고, 정확한 의미 전달을 위해 지연 민감한 데이터로 구성된다. 이와 같은 동영상 데이터를 다양한 대역폭 변화율, 전송중의 높은 패킷 손실율의 특성을 갖는 인터넷을 통해서 전송하기 위해서는 대역폭 적응적이고 에러강인성(Error Resilience)이 높은 시스템이 필요하다. ISO/IEC 의 MPEG-4 에서는 FGS(Fine Grannular scalability)를 표준으로 채택하여 이러한 문제점의 해결방안으로 삼고 있다. FGS 는 기존의 적응적 비디오 코딩의 개념을 적응적 비디오 컨텐츠로 바꾸면서 낮은 복잡도로 대역폭에 적응이 용이하여 다수의 다양한 망 사용자를 모두 만족시킬 수 있어 VoD 나 화상회의 등의 응용에 적합한 기술이라 할 수 있다. 또 인터넷에서 예측하기 어렵게 자주 발생하는 패킷 손실에 대한 오류전파(Error Propagation)가 없는 장점을 가지고 있다. 고용량의 영상 데이터를 다수의 사용자가 동시에 요구하게 되는 상황에서 네트워크 자원을 절약하는 멀티캐스트(Multicast)는 필수적이다. 그리고 비디오와 같은 정보는 그 중요도가 다른 데이터로 구성되므로 특정 상황에서 중요도에 따라 지능적인 처리를 필요로 하는데 차세대 망 기술로 연구되는 Active Network 를 고려 할 수 있다. 영상 정보를 효율적이고 안정적으로 활용하기 위한 이러한 신기술의 효율적인 적용방안을 제안하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5654-5668
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• 2018

Driven by security and real-time demands of Internet of Things (IoT), the timing of fog computing and edge computing have gradually come into place. Gateways bear more nearby computing, storage, analysis and as an intelligent broker of the whole computing lifecycle in between local devices and the remote cloud. In fog computing, the edge broker requires X-aware capabilities that combines software programmability, stream processing, hardware optimization and various connectivity to deal with such as security, data abstraction, network latency, service classification and workload allocation strategy. The prosperous of Field Programmable Gate Array (FPGA) pushes the possibility of gateway capabilities further landed. In this paper, we propose a software-defined gateway (SDG) scheme for fog computing paradigm termed as Fog Computing Zero-Knowledge Gateway that strengthens data protection and resilience merits designed for industrial internet of things or highly privacy concerned hybrid cloud scenarios. It is a proxy for fog nodes and able to integrate with existing commodity gateways. The contribution is that it converts Privacy-Enhancing Technologies rules into provable statements without knowing original sensitive data and guarantees privacy rules applied to the sensitive data before being propagated while preventing potential leakage threats. Some logical functions can be offloaded to any programmable micro-controller embedded to achieve higher computing efficiency.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.65-65
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• 2017

상수관망 시스템(Water Distribution System, WDS)은 원활한 용수 공급을 위해 구축된 사회기반시설물로써, 물 공급절차에 따라 그 구성요소를 공급원, 공급 경로, 수요지 등의 범주로 구분할 수 있다. 원활한 물 공급이란 수요지에서 요구하는 수량과 압력 수준을 충족시키는 것을 의미하며, 따라서 상수관망의 용수공급능력은 요구 수량 및 압력과 실제 공급 결과를 비교함으로써 가늠할 수 있다. 과거에는 두 가지 기준을 별도로 산정하여 이를 평가하였으나, 유량과 압력을 함께 고려할 수 있는 에너지 기반의 평가 방법이 제시되면서 시스템 내 에너지 분포를 정량화하여 시스템의 용수공급능력을 평가하는 연구가 주목받고 있다. 세계적으로 많은 연구자들은 시스템 내 에너지 흐름 상태를 정량화함으로써 다양한 형태의 상수관망의 신뢰도지수(Reliability Index)를 제안한 바 있다. 이 때, 대부분의 신뢰도 지수 연구에서는 수요지에 공급된 에너지를 기본적으로 유지해야 하는 최소요구 에너지(Required Energy)와 비상 상황에 대응하기 위한 잉여 에너지(Surplus Energy)로 구분하고 있으며, 잉여 에너지를 상수관망의 공급 안정성을 나타내는 핵심 요소로 활용하고 있다. 확보된 잉여 에너지는 비상시 최소요구 에너지를 대체하는 개념에서 복원력으로 표현되어, 잘 알려진 Resilience Index(RI)를 비롯해 많은 복원력 지수가 존재한다. 본 연구에서는 복원력 지수를 포함한 세 가지의 신뢰도 지수를 적용하여 상수관망의 용수공급 상황 변화에 따른 시스템의 안정성을 분석하였다. 특히, 절점별 복원력 지수를 산정하고 그 분포를 공간적으로 도시하여 파악함으로써, 비상시 효율적인 운영을 위한 판단기준으로써 신뢰도 지수를 폭 넓게 활용할 수 있음을 제시하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4105-4121
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• 2021

Recently, malware classification based on Deep Neural Networks (DNN) has gained significant attention due to the rise in popularity of artificial intelligence (AI). DNN-based malware classifiers are a novel solution to combat never-before-seen malware families because this approach is able to classify malwares based on structural characteristics rather than requiring particular signatures like traditional malware classifiers. However, these DNN-based classifiers have been found to lack robustness against malwares that are carefully crafted to evade detection. These specially crafted pieces of malware are referred to as adversarial examples. We consider a clever adversary who has a thorough knowledge of DNN-based malware classifiers and will exploit it to generate a crafty malware to fool DNN-based classifiers. In this paper, we propose a DNN-based malware classifier that becomes resilient to these kinds of attacks by exploiting Generative Adversarial Network (GAN) based data augmentation. The experimental results show that the proposed scheme classifies malware, including AEs, with a false positive rate (FPR) of 3.0% and a balanced accuracy of 70.16%. These are respective 26.1% and 18.5% enhancements when compared to a traditional DNN-based classifier that does not exploit GAN.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• Steel and Composite Structures
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• v.52 no.5
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• pp.571-581
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• 2024

After a disaster like the catastrophic earthquake, the government have to use rapid assessment of the condition (or damage) of bridges, buildings and other infrastructures is mandatory for rapid feedbacks, rescue and post-event management. This work studies the tracking control problem of a class of strict-feedback nonlinear systems with input saturation nonlinearity. Under the framework of dynamic surface control design, RBF neural networks are introduced to approximate the unknown nonlinear dynamics. In order to address the impact of input saturation nonlinearity in the system, an auxiliary control system is constructed, and by introducing a class of first-order low-pass filters, the problems of large computation and computational explosion caused by repeated differentiation are effectively solved. In response to unknown parameters, corresponding adaptive updating control laws are designed. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• Journal of KIISE:Information Networking
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• v.30 no.6
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• pp.695-704
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• 2003

A wavelet-based video stream is more susceptible to the network transmission errors than DCT-based video. This is because bit-errors in a subband of a video frame affect not only the other subbands within the current frame but also the subsequent frames. In this paper, we propose a video source coding scheme called IPC(Intra Prediction Coding) scheme in order to reduce the error propagation to the subsequent frames. In the proposed scheme, a subband except LL subband in the current frame refers to the lower-level subband within the same frame. This reduces the error propagation to subsequent frames. We evaluated the performance of our proposed scheme in the simulated wireless network environment. As a result of tests, it was shown that the proposed algorithm shows better performance than MRME in a heavy motion image sequence while IPC outperforms MRME at a high bit-rate in small motion image sequence.

• The KIPS Transactions:PartC
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• v.13C no.2 s.105
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• pp.195-202
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• 2006

For applying sensor networking technology for our daily life, security service is essential, and pairwise key establishment is the key point for security. In this paper, we propose fairwise key establishment mechanism for secure coumunication in sensor networks. In the mechanism, we cluster the network field before deployment and predistribute key materials to normal sensor nodes and clusterheads. For clusterheads, more key materials are predistributed, and after deployment, sensor nodes which need to establish pairwise keys with other sensor nodes in different clusters make request for related key materials to their own clusterheads. Our proposal reduces the memory requirements for normal sensor nodes by distributing more information to clusterheads, and it raises the security level and resilience against node captures. In addition, it guarantees perfect pairwise key establishments for every pair of neighboring nodes and provides efficient and secure sensor communications.