• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.65-72
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• 2023

Cloud Computing is one of the current research areas in computer science. Recently, Cloud is the buzz word used everywhere in IT industries; It introduced the notion of 'pay as you use' and revolutionized developments in IT. The rapid growth of modernized cloud computing leads to 24×7 accessing of e-resources from anywhere at any time. It offers storage as a service where users' data can be stored on a cloud which is managed by a third party who is called Cloud Service Provider (CSP). Since users' data are managed by a third party, it must be encrypted ensuring confidentiality and privacy of the data. There are different types of cryptographic algorithms used for cloud security; in this article, the algorithms and their security measures are discussed.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.33-40
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• 2023

The mainstay of current image recovery frameworks is Content-Based Image Retrieval (CBIR). The most distinctive retrieval method involves the submission of an image query, after which the system extracts visual characteristics such as shape, color, and texture from the images. Most of the techniques use RGB color space to extract and classify images as it is the default color space of the images when those techniques fail to change the color space of the images. To determine the most effective color space for retrieving images, this research discusses the transformation of RGB to different color spaces, feature extraction, and usage of Convolutional Neural Networks for retrieval.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.84-90
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• 2023

In this article the problem of computing floating-point reciprocal cube root functions is considered. Our new algorithms for this task decrease the number of arithmetic operations used for computing $1/{\sqrt[3]{x}}$. A new approach for selection of magic constants is presented in order to minimize the computation time for reciprocal cube roots of arguments with movable decimal point. The underlying theory enables partitioning of the base argument range x∈[1,8) into 3 segments, what in turn increases accuracy of initial function approximation and decreases the number of iterations to one. Three best algorithms were implemented and carefully tested on 32-bit microcontroller with ARM core. Their custom C implementations were favourable compared with the algorithm based on cbrtf(x) function taken from C ＜math.h＞ library on three different hardware platforms. As a result, the new fast approximation algorithm for the function $1/{\sqrt[3]{x}}$ was determined that outperforms all other algorithms in terms of computation time and cycle count.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.06a
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• pp.481-487
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• 2001

본 논문은 현재까지 제시되어진 peer to peer network model들을 정리하고 대표적인 peer to peer network model에 지문인식을 적용하여 개인에 대한 신원 인증 절차를 수행함으로써 보안에 대한 새로운 해결책을 제안하였다. 기존의 peer to peer network model은 개인 대 개인간의 효율적인 network검색 기능과 분산 computing 환경을 제공하지만 보안에 관해서는 아직까지도 많은 연구가 필요하다. 본 연구에서는 기존의 peer to peer network model들에 지문인식을 사용한 새로운 보안 model을 설계하였다.

• Smart Media Journal
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• v.6 no.1
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• pp.9-15
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• 2017

The trends in ICT are concentrated in IoT, Bigdata, and Cloud Computing. These mega-trends do not operate independently, and mass storage technology is essential as large computing technology is needed in the background to support them. In order to evaluate the performance of high-capacity storage based on open source Ceph, we carry out the demonstration test of Abyss Storage with domestic and overseas sites using educational network KOREN. In addition, storage media and network bonding are tested to evaluate the performance of the storage itself. Although there is a substantial difference in aspect of the physical speed among storage medias, there is no significant performance difference in the storage media test performed. As a solution to this problem, we could get performance improvement through network acceleration. In addition, we conducted actual performance test of Abyss Storage internal and external network by connecting domestic and overseas sites using KOREN network.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.11
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• pp.71-79
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• 2017

In this paper, we implemented the exchange rate forecasting neural network using heterogeneous computing. Exchange rate forecasting requires a large amount of data. We used a neural network that could leverage this data accordingly. Neural networks are largely divided into two processes: learning and verification. Learning took advantage of the CPU. For verification, RTL written in Verilog HDL was run on FPGA. The structure of the neural network has four input neurons, four hidden neurons, and one output neuron. The input neurons used the US $ 1, Japanese 100 Yen, EU 1 Euro, and UK £ 1. The input neurons predicted a Canadian dollar value of $ 1. The order of predicting the exchange rate is input, normalization, fixed-point conversion, neural network forward, floating-point conversion, denormalization, and outputting. As a result of forecasting the exchange rate in November 2016, there was an error amount between 0.9 won and 9.13 won. If we increase the number of neurons by adding data other than the exchange rate, it is expected that more precise exchange rate prediction will be possible.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.549-559
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• 2023

As mobile edge computing (MEC) is gaining attention as a core technology of 5G networks, edge AI technology of 5G network environment based on mobile user data is recently being used in various fields. However, as in traditional AI security, there is a possibility of adversarial interference of standard 5G network functions within the core network responsible for edge AI core functions. In addition, research on data poisoning attacks that can occur in the MEC environment of standalone mode defined in 5G standards by 3GPP is currently insufficient compared to existing LTE networks. In this study, we explore the threat model for the MEC environment using NWDAF, a network function that is responsible for the core function of edge AI in 5G, and propose a feature selection method to improve the performance of detecting data poisoning attacks for Leaf NWDAF as some proof of concept. Through the proposed methodology, we achieved a maximum detection rate of 94.9% for Slowloris attack-based data poisoning attacks in NWDAF.

• 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.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.1-8
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• 2020

In order to reach Ultra-Reliable Low-Latency communication, one of 5G aims, Multi-access Edge Computing paradigm was born. The idea of this paradigm is to bring cloud computing technologies closer to the network edge. User services are hosted in multiple Edge Clouds, deployed at the edge of the network distributedly, to reduce the service latency. For mobile users, migrating their services to the most proper Edge Clouds for maintaining a Quality of Service is a non-convex problem. The service migration problem becomes more complex in high mobility scenarios. The goal of the study is to observe how user mobility affects the selection of Edge Cloud during a fixed mobility path. Mobility-Aware Service Migration (MASM) is proposed to optimize service migration based on two main parameters: routing cost and service migration cost, during a high mobility scenario. The performance of the proposed algorithm is compared with an existing greedy algorithm.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.25-37
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• 2019

Inspired by the rapid development of deep learning and online biometrics-based authentication, we propose a handwritten one-time password authentication system which employs deep learning-based handwriting recognition and writer verification techniques. We design a convolutional neural network to recognize handwritten digits and a Siamese network to compute the similarity between the input handwriting and the genuine user's handwriting. We propose the first application of the second edition of NIST Special Database 19 for a writer verification task. Our system achieves 98.58% accuracy in the handwriting recognition task, and about 93% accuracy in the writer verification task based on four input images. We believe the proposed handwriting-based biometric technique has potential for use in a variety of online authentication services under the FIDO framework.