• The Journal of Bigdata
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• v.2 no.2
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• pp.1-7
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• 2017

Intelligent transportation system (ITS) has been introduced to maximize the efficiency of operation and utilization of the urban traffic facilities and promote the safety and convenience of the users. With the expansion of ITS, various traffic big data such as road traffic situation, traffic volume, public transportation operation status, management situation, and public traffic use status have been increased exponentially. In this paper, we derive structural characteristics of urban traffic according to the vehicle flow by using big data network analysis. DSRC (Dedicated Short Range Communications) data is used to construct the traffic network. The results can help to understand the complex urban traffic characteristics more easily and provide basic research data for urban transportation plan such as road congestion resolution plan, road expansion plan, and bus line/interval plan in a city.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.181-184
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• 2000

In this paper, the design of WDM network using the traffic estimation modeling is implemented. Because of the lack of data of real traffic volumes, the information of statistic data is used. using the modeling results, the WDM channels is assigned for each node, and the network is simulated using OPNET simulation tools. As a result, the realistic WDM network design for Korea topology is proposed.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.52-60
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• 2024

APT (Advanced Persistent Threat) attack is a dangerous, targeted attack form with clear targets. APT attack campaigns have huge consequences. Therefore, the problem of researching and developing the APT attack detection solution is very urgent and necessary nowadays. On the other hand, no matter how advanced the APT attack, it has clear processes and lifecycles. Taking advantage of this point, security experts recommend that could develop APT attack detection solutions for each of their life cycles and processes. In APT attacks, hackers often use phishing techniques to perform attacks and steal data. If this attack and phishing phase is detected, the entire APT attack campaign will be crash. Therefore, it is necessary to research and deploy technology and solutions that could detect early the APT attack when it is in the stages of attacking and stealing data. This paper proposes an APT attack detection framework based on the Network traffic analysis technique using open-source tools and deep learning models. This research focuses on analyzing Network traffic into different components, then finds ways to extract abnormal behaviors on those components, and finally uses deep learning algorithms to classify Network traffic based on the extracted abnormal behaviors. The abnormal behavior analysis process is presented in detail in section III.A of the paper. The APT attack detection method based on Network traffic is presented in section III.B of this paper. Finally, the experimental process of the proposal is performed in section IV of the paper.

• ETRI Journal
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• v.46 no.3
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7B
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• pp.641-649
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• 2004

We propose a dynamic traffic management model which uses adaptive priority reassignment algorithm to deliver service differentiation in home networks, and implement adaptive priority reassignment algorithm using FPGA. The proposed architecture is designed to handle home network traffic without the need for signaling protocol. We categorize home network traffic into three kinds of traffic class: control data traffic class, the Internet data and non-real-time data traffic class, and multimedia data traffic class (include non-real-time and real-time multimedia data traffic). To support differential service about these kinds of traffic class, we designed and implemented a traffic management framework that dynamically change each traffic class priority depending on bandwidth utilization of each traffic class.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2097-2100
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• 2003

Many studies have been done on secure operating system using secure kernel that has various access control policies for system security. Secure kernel can protect user or system data from unauthorized and/or illegal accesses by applying various access control policies like DAC(Discretionary Access Control), MAC(Mandatory Access Control), RBAC(Role Based Access Control), and so on. But, even if secure operating system is running under various access control policies, network traffic among these secure operating systems can be captured and exposed easily by network monitoring tools like packet sniffer if there is no protection policy for network traffic among secure operating systems. For this reason, protection for data within network traffic is as important as protection for data within local system. In this paper, we propose a secure operating system trusted channel, SOSTC, as a prototype of a simple secure network protocol that can protect network traffic among secure operating systems and can transfer security information of the subject. It is significant that SOSTC can be used to extend a security range of secure operating system to the network environment.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• Proceedings of KOSOMES biannual meeting
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• 1999.03a
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• pp.93-105
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• 1999

In Vessel Traffic Service, the management information on marine traffic control is almost transported by VHF. It is so difficult to exchange a lot of the related information necessary for marine traffic control exactly and in real time. Aiming at improved visualized data transporting network, we examine the methods for transporting and displaying the data on marine traffic controls. In this paper, we design the information networks established by broadcasting method and by internet method using home page in order to manage marine traffics in Masan port.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2964-2985
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• 2019

Global data center IP traffic is expected to reach 20.6 zettabytes (ZB) by the end of 2021. Intra-data center networks (Intra-DCN) will account for 71.5% of the data center traffic flow and will be the largest portion of the traffic. The understanding of traffic distribution in IntraDCN is still sketchy. It causes significant amount of bandwidth to go unutilized, and creates avoidable choke points. Conventional transport protocols such as Optical Packet Switching (OPS) and Optical Burst Switching (OBS) allow a one-sided view of the traffic flow in the network. This therefore causes disjointed and uncoordinated decision-making at each node. For effective resource planning, there is the need to consider joining the distributed with centralized management which anticipates the system's needs and regulates the entire network. Methods derived from Kalman filters have proved effective in planning road networks. Considering the network available bandwidth as data transport highways, we propose an intelligent enhanced SDN concept applied to OBS architecture. A management plane (MP) is added to conventional control (CP) and data planes (DP). The MP assembles the traffic spatio-temporal parameters from ingress nodes, uses Kalman filtering prediction-based algorithm to estimate traffic demand. Prior to packets arrival at edges nodes, it regularly forwards updates of resources allocation to CPs. Simulations were done on a hybrid scheme (1+1) and on the centralized OBS. The results demonstrated that the proposition decreases the packet loss ratio. It also improves network latency and throughput-up to 84 and 51%, respectively, versus the traditional scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.772-776
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• 2005

To support abruptly increasing data traffic in these days, network management is needed. And also to maintain the steady infra, there is MPLS need which can support traffic engineering. It's necessary that MPLS doesn't only manage network to support recently booming data traffic, but has capacity to support traffic engineering to keep static infrastructure. Traffic engineering, method that a large-scale user shifts traffic to the beforehand designated routes that pass through specific nodes on network, is operation that is mapping traffic flow to the physical network topology. In this paper, we supplement the defect of the traditional RSVP traffic engineering and to construct far more steady infra, we suggest the way of its development of ERSVP signaling protocol.