• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.267-273
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• 2011

The importance of networking capability is gaining more weight for enterprise business and high-speed Internet access with guaranteed security management is essential to companies. This paper presents a unified network security management solution to support high-speed Internet access, active security management, traffic classification and control. The presented system provides firewall, VPN, intrusion detection, contents filtering, traffic management, QoS management, and history log functions in unified manner implemented in a single appliance device located at the edge of enterprise networks. This will enable cost effective unified network security solution to companies.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.95-98
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• 2021

최근 비정상적인 네트워크 활동 감지 및 네트워크 서비스 프로비저닝과 같은 다양한 분야에서 응용되는 네트워크 트래픽 예측 기술이 네트워크 통신 문제에 의한 트래픽의 결측 및 네트워크 유저의 불규칙한 활동에 의한 비선형 특성 때문에 발생하는 성능 저하를 극복하기 위해 딥러닝 신경망에 대한 연구가 활성화되고 있다. 이 딥러닝 신경망 중 시계열 딥러닝 신경망은 단기 네트워크 트래픽 볼륨을 예측할 때 낮은 오류율을 보인다. 하지만, 시계열 딥러닝 신경망은 기울기 소멸 및 폭발과 같은 비선형성, 다중 계절성 및 장기적 의존성 문제와 같은 한계를 보여준다. 이 논문에서는 계절성 임베딩을 고려한 주의 신경망 기반 트래픽 예측 기법을 제안한다. 제안하는 기법은 STL 분해 기법을 통해 분해된 트래픽 트랜드, 계절성, 잔차를 이용하여 일별 및 주별 계절성을 임베딩하고 이를 주의 신경망을 기반으로 향후 트래픽을 예측한다.

• Journal of KIISE:Information Networking
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• v.33 no.3
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• pp.201-217
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• 2006

In this paper, we collected the physical traces from high speed Internet backbone traffic and analyze the various characteristics of the underlying packet traces. Particularly, our work is focused on analyzing the characteristics of an anomalous traffic. It is found that in our data, the anomalous traffic is caused by UDP session traffic and we determined that it was one of the Denial of Service attacks. In this work, we adopted the unsupervised machine learning algorithm to classify the network flows. We apply the k-means clustering algorithm to train the learner. Via the Cramer-Yon-Misses test, we confirmed that the proposed classification method which is able to detect anomalous traffic within 1 second can accurately predict the class of a flow and can be effectively used in determining the anomalous flows.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11B
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• pp.1038-1049
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• 2003

Exactly measuring traffic load is the basis for efficient traffic engineering. However, precise traffic measurement involves inspecting every packet traversing a lint resulting in significant overhead on routers with high-speed links. Sampling techniques are proposed as an alternative way to reduce the measurement overhead. But, since sampling inevitably accompany with error, there should be a way to control, or at least limit, the error for traffic engineering applications to work correctly. In this paper, we address the problem of bounding sampling error within a pre-specified tolerance level. We derive a relationship between the number of samples, the accuracy of estimation and the squared coefficient of variation of packet size distribution. Based on this relationship, we propose an adaptive random sampling technique that determines the minimum sampling probability adaptively according to traffic dynamics. Using real network traffic traces, we show that the proposed adaptive random sampling technique indeed produces the desired accuracy, while also yielding significant reduction in the amount of traffic samples.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.172-181
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• 2005

The latest attack type against network traffic appeared by worm and bot that are advanced in DDoS. It is difficult to detect them because they are diversified, intelligent, concealed and automated. The exisiting traffic analysis method using SNMP has a vulnerable problem; it considers normal P2P and other application program to be harmful traffic. It also has limitation that does not analyze advanced programs such as worm and bot to harmful traffic. Therefore, we analyzed harmful traffic out Protocol and Port analysis. We also classified traffic by protocol, well-known port, P2P port, existing attack port, and specification port, apply singularity weight to detect, and analyze attack availability. As a result of simulation, it is proved that it can effectively detect P2P application, worm, bot, and DDoS attack.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10B
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• pp.947-956
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• 2003

The current IP Routing Protocolspacket networks also need to provide the network QoS based of DiffServ, RSVP, MPLStraffic model which is standardized as IETF reference model for NGN. The first topic of this paper is to propose Traffic-Balanced Routing Protocol(TBRP) to process existing best effort traffic. TBRP will process low priority interactive data and background data which is not sensitive to dealy. Secondly Hierarchical Traffic-Traffic-Scheduling Routing Protocol(HTSRP) is also proposed. HTSRP is the hierarchical routing algorithm for backbone and access networkin case of fixed-wireless convergence network. Finally, HTSRP_Q is proposed to meet the QoS requirement when user want interactive or streaming packet service. This protocol will maximize the usage of resources of access layer based on the QoS parameters and process delay-sensitive traffic. Service classes are categorized into 5 types by the user request, such as conversational, streaming, high priority interactive, low priority interactive, and background class. It could be processed efficiently by the routing protocolstraffic model proposed in this paper. The proposed routing protocolstraffic model provides the increase of efficiency and stability of the next generation network thanks to the routing according to the characteristic of the specialized service categories.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.1-8
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• 2015

Security has become an issue due to the rapid increases in internet traffic data network. Especially P2P traffic data poses a great challenge to network systems administrators. Preemptive measures are necessary for network quality of service(QoS) and efficient resource management like blocking suspicious traffic data. Deep packet inspection(DPI) is the most exact way to detect an intrusion but it may pose a private security problem that requires time. We used several machine learning methods to compare the performance in classifying network traffic data accurately over time. The Random Forest method shows an excellent performance in both accuracy and time.

• Journal of the Korea Computer Industry Society
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• v.3 no.7
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• pp.915-920
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• 2002

The classical queuing analysis has been tremendously useful in doing capacity planning and performance prediction. However, in many real-world cases. it has found that the predicted results form a queuing analysis differ substantially from the actual observed performance. Specially, in recent years, a number of studies have demonstrated that for some environments, the traffic pattern is self-similar rather than Poisson. In this paper, we study these self-similar traffic characteristics and the definition of self-similar stochastic processes. Then, we consider the examples of self-similar data traffic, which is reported from recent measurement studies. Finally, we wish yon that it makes out about the characteristics of actual data traffic more easily.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.01a
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• pp.217-218
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• 2012

기기간(M2M, Machine-to-Machin) 통신의 한 응용으로서 스마트 그리드 네트워크는 다수의 기기 통신으로 인한 전송 데이터의 방대한 양을 대표적 특징으로 꼽을 수 있다. 이에 현재 사용가능한 통신 기술들을 그대로 사용할 경우, 병목현상 혹은 네트워크 혼잡 등 네트워크 장애 및 전송 지연이 발생할 수 있다. 특히 스마트 그리드 네트워크의 상향 트래픽은 시간조절이 가능한 주기적 미터링 데이터와 지연민감한 이벤트 데이터로 나뉜다. 이에 본 논문에서는 각 트래픽 특성에 따라 트래픽양의 대다수를 이룰 미터링 데이터의 전송시간 조절을 이용한 혼잡제어 기법을 제안한다. 이를 통해 지연민감한 이벤트 데이터의 지연시간 내 전송 보장 확률을 높이고, 트래픽을 분산시킴으로써 전송 효율을 높이고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.468-471
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• 2001

The classical queuing analysis has been tremendously useful in doing capacity planning and performance prediction. However, in many real-world cases. it has found that the predicted results form a queuing analysis differ substantially from the actual observed performance. Specially, in recent years, a number of studies have demonstrated that for some environments, the traffic pattern is self-similar rather than Poisson. In this paper, we study these self-similar traffic characteristics and the definition of self-similar stochastic processes. Then, we consider the examples of self-similar data traffic, which is reported from recent measurement studies. Finally, we wish you that it makes out about the characteristics of actual data traffic more easily.