• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.590-593
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• 2012

현재 인터넷은 하드웨어 종속적인 특징을 가지고 있어 급변하는 환경에 적응하기 힘들다. 이러한 제약사항은 관련 산업 발전을 더디게 하고 있다. 이와 같은 네트워크 환경에서 산업 발전을 위하여 네트워크 인프라에 유연성을 제공할 수 있는 기술의 개발이 필요하다. 그러한 문제를 해결하기 위해 오픈프로토콜인 OpenFlow의 Programmable Network의 특성을 이용하여 네트워크 가상화를 구현하였으며, 응용 서비스별 Virtual Network를 제공하는 방안에 대해 연구하였다. 이를 위하여 OpenFlow 기반의 Programmable Network를 구축하였으며, 동적으로 구성이 가능한 네트워크에서 가상화를 제공하기 위해 VNAPI를 개발하였다. 또한, VNAPI를 통하여 신뢰성 있고 효율적인 응용 서비스의 전달을 위하여 Virtual Network Topology에 대한 설계를 같이 수행하였다.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.168-174
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• 2021

The Cognitive radio (CR) is evolving technology for managing the spectrum bandwidth in wireless network. The security plays a vital role in wireless network where the secondary users are trying to access the primary user's bandwidth. During the allocation the any malicious user either he pretends to be primary user or secondary user to access the vital information's such as credentials, hacking the key, network jam, user overlapping etc. This research paper discusses on various types of attack and to prevent the attack in cognitive radio network. In this research, secondary users are identified by the primary user to access the primary network by the secondary users. The secondary users are given authorization to access the primary network. If any secondary user fails to provide the authorization, then that user will be treated as the malicious user. In this paper two approaches are suggested one by applying elliptic curve cryptography and the other method by using priority-based service access.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.713-724
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• 2016

Energy consumption has become a main problem of sustainable development in communication networks and how to communicate with high energy efficiency is a significant topic that researchers and network operators commonly concern. In this paper, an energy-efficient multicast algorithm in multi-hop wireless networks is proposed aiming at new generation wireless communications. Traditional multi-hop wireless network design only considers either network efficiency or minimum energy consumption of networks, but rarely the maximum energy efficiency of networks. Different from previous methods, the paper targets maximizing energy efficiency of networks. In order to get optimal energy efficiency to build network multicast, our proposed method tries to maximize network throughput and minimize networks' energy consumption by exploiting network coding and sleeping scheme. Simulation results show that the proposed algorithm has better energy efficiency and performance improvements compared with existing methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.362-364
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• 2018

Predicting network traffic volume has become a popular topic recently due to its support in many situations such as detecting abnormal network activities and provisioning network services. Especially, predicting the volume of the next upcoming traffic from the series of observed recent traffic volume is an interesting and challenging problem. In past, various techniques are researched by using time series forecasting methods such as moving averaging and exponential smoothing. In this paper, we propose a long short-term memory neural network (LSTM) based network traffic volume prediction method. The proposed method employs the changing rate of observed traffic volume, the corresponding time window index, and a seasonality factor indicating the changing trend as input features, and predicts the upcoming network traffic. The experiment results with real datasets proves that our proposed method works better than other time series forecasting methods in predicting upcoming network traffic.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.896-915
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• 2023

The network function virtualization (NFV) uses virtualization technology to separate software from hardware. One of the most important challenges of NFV is the resource management of virtual network functions (VNFs). According to the dynamic nature of NFV, the resource allocation of VNFs must be changed to adapt to the variations of incoming network traffic. However, the significant delay may be happened because of the reallocation of resources. In order to balance the performance between delay and quality of service, this paper firstly made a compromise between VNF migration and energy consumption. Then, the long short-term memory (LSTM) was utilized to forecast network traffic. Also, the asymmetric loss function for LSTM (LO-LSTM) was proposed to increase the predicted value to a certain extent. Finally, an experiment was conducted to evaluate the performance of LO-LSTM. The results demonstrated that the proposed LO-LSTM can not only reduce migration times, but also make the energy consumption increment within an acceptable range.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.63-66
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• 2021

The technological advancement in computer vision has made system like grab-and-go grocery a reality. Now all the shoppers have to do now is to walk in grab the items and go out without having to wait in the long queues. This paper presents an intelligent retail environment system that is capable of monitoring and tracking customer's activity during shopping based on their interaction with the shelf. It aims to develop a system that is low cost, easy to mount and exhibit adequate performance in real environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.467-473
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• 2011

The Network-packet in this Paper to ensure the integrity of the legal evidence is effect that can have is to offer an Network-forensics system. The Paper proposed Network-forensics system in the company through legal disputes accident Networking and state agency (with investigative authority) for criminal investigations in networking for the effective and correct way to present a report of user-centric services through effective awareness can be improved.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.18
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• pp.7775-7780
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• 2014

This paper addresses cancer prediction based on radial basis function neural network optimized by particle swarm optimization. Today, cancer hazard to people is increasing, and it is often difficult to cure cancer. The occurrence of cancer can be predicted by the method of the computer so that people can take timely and effective measures to prevent the occurrence of cancer. In this paper, the occurrence of cancer is predicted by the means of Radial Basis Function Neural Network Optimized by Particle Swarm Optimization. The neural network parameters to be optimized include the weight vector between network hidden layer and output layer, and the threshold of output layer neurons. The experimental data were obtained from the Wisconsin breast cancer database. A total of 12 experiments were done by setting 12 different sets of experimental result reliability. The findings show that the method can improve the accuracy, reliability and stability of cancer prediction greatly and effectively.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.351-363
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• 2008

This paper is concerned with tasking and guidance of networked airborne sensors to achieve fault-tolerant sensing. The sensors are coordinated to locate hostile transmitters by intercepting and processing their signals. Faults occur when some sensor-carrying vehicles engaged in target location missions are lost. Faults effectively change the network architecture and therefore degrade the network performance. The first objective of the paper is to optimally allocate a finite number of sensors to targets to maximize the network life and availability. To that end allocation policies are solved from relevant Markov decision problems. The sensors allocated to a target must continue to adjust their trajectories until the estimate of the target location reaches a prescribed accuracy. The second objective of the paper is to establish a criterion for vehicle guidance for which fault-tolerant sensing is achieved by incorporating the knowledge of vehicle loss probability, and by allowing network reconfiguration in the event of loss of vehicles. Superior sensing performance in terms of location accuracy is demonstrated under the established criterion.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.65-68
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• 2002

Distributed Generators (DG) are rapidly increasing and most of them are interconnected with distribution network to supply power into the network. Therefore, DG may make significant impacts on distribution system operation. protection, and control with respect to the voltage regulation, voltage flicker, harmonics, fault current levels, the losses of the network, etc. These impacts would be demerits for both of DG and distribution networks. And the operation of DG may be influenced by the abnormal grid condition such as disturbances occurred in the neighboring distribution feeders as well as the feeder directly connected with DG. This paper describes the influence of fault occurred in the interconnected power network on the DG operation and the impact of DG on the network load during the interruptions of utility power.