• Journal of Communications and Networks
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• v.13 no.6
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• pp.602-611
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• 2011

The inter-domain routing scalability issue is a major challenge facing the Internet. Recent wide deployments of multihoming and traffic engineering urge for solutions to this issue. So far, tunnel-based proposals and compact routing schemes have been suggested. An implicit assumption in the routing community is that structured address labels are crucial for routing scalability. This paper first systematically examines the properties of identifiers and address labels and their functional differences. It develops a simple Internet routing model and shows that a binary relation T defined on the address label set A determines the cardinality of the compact label set L. Furthermore, it is shown that routing schemes based on flat address labels are not scalable. This implies that routing scalability and routing stability are inherently related and must be considered together when a routing scheme is evaluated. Furthermore, a metric is defined to measure the efficiency of the address label coding. Simulations show that given a 3000-autonomous system (AS) topology, the required length of address labels in compact routing schemes is only 9.12 bits while the required length is 10.64 bits for the Internet protocol (IP) upper bound case. Simulations also show that the ${\alpha}$ values of the compact routing and IP routing schemes are 0.80 and 0.95, respectively, for a 3000-AS topology. This indicates that a compact routing scheme with necessary routing stability is desirable. It is also seen that using provider allocated IP addresses in multihomed stub ASs does not significantly reduce the global routing size of an IP routing system.

• The Journal of Bigdata
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• v.7 no.2
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• pp.1-9
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• 2022

In the global era, the importance of imported food safety management is increasing. Address information of overseas food companies is key information for imported food safety management, and must be verified for prompt response and follow-up management in the event of a food risk. However, because each country's address system is different, one verification system cannot verify the addresses of all countries. Also, the purpose of address verification may be different depending on the field used. In this paper, we deal with the problem of classifying a given overseas food business address into the administrative district level of the country. This is because, in the event of harm to imported food, it is necessary to find the administrative district level from the address of the relevant company, and based on this trace the food distribution route or take measures to ban imports. However, in some countries the administrative district level name is omitted from the address, and the same place name is used repeatedly in several administrative district levels, so it is not easy to accurately classify the administrative district level from the address. In this study we propose a deep learning-based administrative district level classification model suitable for this case, and verify the actual address data of overseas food companies. Specifically, a method of training using a label powerset in a multi-label classification model is used. To verify the proposed method, the accuracy was verified for the addresses of overseas manufacturing companies in Ecuador and Vietnam registered with the Ministry of Food and Drug Safety, and the accuracy was improved by 28.1% and 13%, respectively, compared to the existing classification model.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.2
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• pp.30-37
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• 2003

To effectively control a vast amount of postal packages, we need the automatic system for extracting the address region from CCD scanner images. In this paper, we propose a address region extraction algorithm in the standard postal label. We used geometric characteristics of the underlying address regions and defined several criteria for fast detection of address regions. As a result, we accomplished a successful detection and classification of the postal package labels in real time.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.527-538
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• 2004

In this paper, we present the flow aggregation method and the FLTC(flow lasting time control) algorithm to reduce the number of flows and solve the scalability problem in high speed IP switching networks. The flow aggregation based on the destination address could reduce the total number of flows, improve the label efficiency, and increase the total amount of the switched packets. The FLTC algorithm also eliminates the waste of label by deleting the flow binding efficiently. With the traces of real Internet traffics, we evaluate the performance of these schemes by simulation. The label efficiency, the average number of label used, and the percentage of packets switched and the number of packets switched are used as performance measures for this simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4665-4683
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• 2019

Previous methods build image annotation model by leveraging three basic dependencies: relations between image and label (image/label), between images (image/image) and between labels (label/label). Even though plenty of researches show that multiple dependencies can work jointly to improve annotation performance, different dependencies actually do not "work jointly" in their diagram, whose performance is largely depending on the result predicted by image/label section. To address this problem, we propose the adaptive attention annotation model (AAAM) to associate these dependencies with the prediction path, which is composed of a series of labels (tags) in the order they are detected. In particular, we optimize the prediction path by detecting the relevant labels from the easy-to-detect to the hard-to-detect, which are found using Binary Cross-Entropy (BCE) and Triplet Margin (TM) losses, respectively. Besides, in order to capture the inforamtion of each label, instead of explicitly extracting regional featutres, we propose the self-attention machanism to implicitly enhance the relevant region and restrain those irrelevant. To validate the effective of the model, we conduct experiments on three well-known public datasets, COCO 2014, IAPR TC-12 and NUSWIDE, and achieve better performance than the state-of-the-art methods.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10a
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• pp.386-388
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• 1998

Multiprotocol Label Switching(MPLS)는 기존 인터넷 라우팅에서 사용하는 longest prefix match 방식 대신에 short label exact match 방식을 사용함으로써 더 단순한 고속 포워딩 기술을 제공한다. 또한, 동일한 Forwarding Equivalence Class(FEC)에속하는 플로우들을 합성하여 하나의 레이블을 사용하는 방법 즉, Multipoint-to-point Tree(MPT)를 형성함으로써 확장설을 향상시켰다. 본 논문에선 현재 이슈가 괴고 있는 "IP address prefix"와 "host LSP"의 FEC타입과는 전송방식이 다른 브로드캐스팅을 위한"broadcast"FEC 타입을 제안하였으며, 브로드캐스트 패킷을 전송하는데 있어서 각각의Label Switched Path(LSP)제어 방식을 사용할 경우의 문제점을 분석하고, 유니캐스팅과 브로드캐스팅의 서로 다른 레이블 할당방식으로 인한 레이블 충돌을 해결하기 위해서 레이블 범위를 분류하여 사용하는 방식을 제안한다.해서 레이블 범위를 분류하여 사용하는 방식을 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.6 no.4
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• pp.68-73
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• 2001

MPLS(Multi protocol Label Switching) is standard skill for added to speed and control the Network Traffic. MPLS concerned the routing protocol to relative Pack line, Each Pack composed label and node, saved the time to seek the address of node. MPLS worked IP, ATM and Network protocol of flame rely. MPLS is Network OSI suport model, 2Layer send to most of Packinsted of 3Layer Switching. MPLS is added speed Traffic of QoS and effective controled the Network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1998.11a
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• pp.354-357
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• 1998

MPLS (Multiprotocol Label Switching)는 인터넷에서 속도, 확장성 그리고 서비스 제공 능력을 향상시키기 위한 백본 네트웍으로 이용하기 위하여 Internet Engineering Task Force (IETF) standard로 급속하게 성장하고 있다. MPLS는 기존의 인터넷 라우팅에서 사용하는 longest prefix match 방식을 이용하는 라우팅 방법 대신에 short label exact match 방식과 L3 forwarding 방법을 사용함으로써 고속 인터넷 서비스 기술을 제공한다. 한편, 기존 인터넷의 IP format을 보면 Host Id, (Address prefix, Host Id), Broadcasting, Multicasting의 네 가지 종류의 구조를 가지고 있다. 그러나 아직 MPLS에서의 멀티캐스트 서비스에 대한 방식이 아직 표준화되고 있지 않고 있는 상황에서, MPLS 서비스 도메인 내에서 멀티캐스트 패킷은 기존의 멀티캐스트 라우팅 프로토콜과 연계되어 MPLS상에서 멀티캐스트 서비스가 이루어져야 한다. 본 논문에서는 기존의 인터넷상에서 제공되어지는 멀티캐스트 IP 플로우들을 MPLS 상에서 수용하기 위한 방식을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2895-2921
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• 2018

Security has become one of the major concerns in mobile adhoc networks (MANETs). Data and voice communication amongst roaming battlefield entities (such as platoon of soldiers, inter-battlefield tanks and military aircrafts) served by MANETs throw several challenges. It requires complex securing strategy to address threats such as unauthorized network access, man in the middle attacks, denial of service etc., to provide highly reliable communication amongst the nodes. Intrusion Detection and Prevention System (IDPS) undoubtedly is a crucial ingredient to address these threats. IDPS in MANET is managed by Command Control Communication and Intelligence (C3I) system. It consists of networked computers in the tactical battle area that facilitates comprehensive situation awareness by the commanders for timely and optimum decision-making. Key issue in such IDPS mechanism is lack of Smart Learning Engine. We propose a novel behavioral based "Smart Multi-Instance Multi-Label Intrusion Detection and Prevention System (MIML-IDPS)" that follows a distributed and centralized architecture to support a Robust C3I System. This protocol is deployed in a virtually clustered non-uniform network topology with dynamic election of several virtual head nodes acting as a client Intrusion Detection agent connected to a centralized server IDPS located at Command and Control Center. Distributed virtual client nodes serve as the intelligent decision processing unit and centralized IDPS server act as a Smart MIML decision making unit. Simulation and experimental analysis shows the proposed protocol exhibits computational intelligence with counter attacks, efficient memory utilization, classification accuracy and decision convergence in securing C3I System in a Tactical Battlefield environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2607-2627
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• 2017

Nonnegative matrix factorization (NMF) has received considerable attention due to its effectiveness of reducing high dimensional data and importance of producing a parts-based image representation. Most of existing NMF variants attempt to address the assertion that the observed data distribute on a nonlinear low-dimensional manifold. However, recent research results showed that not only the observed data but also the features lie on the low-dimensional manifolds. In addition, a few hard priori label information is available and thus helps to uncover the intrinsic geometrical and discriminative structures of the data space. Motivated by the two aspects above mentioned, we propose a novel algorithm to enhance the effectiveness of image representation, called Dual graph-regularized Constrained Nonnegative Matrix Factorization (DCNMF). The underlying philosophy of the proposed method is that it not only considers the geometric structures of the data manifold and the feature manifold simultaneously, but also mines valuable information from a few known labeled examples. These schemes will improve the performance of image representation and thus enhance the effectiveness of image classification. Extensive experiments on common benchmarks demonstrated that DCNMF has its superiority in image classification compared with state-of-the-art methods.