• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.5
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• pp.429-437
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• 2015

DDoS attacks have been used for paralyzing popular Internet services. Especially, amplification attacks have grown dramatically in recent years. Defending against amplification attacks is challenging since the attacks usually generate extremely hugh amount of traffic and attack traffic is coming from legitimate servers, which is hard to differentiate from normal traffic. Moreover, some of protocols used by amplification attacks are widely adopted in IoT devices so that the number of servers susceptible to amplification attacks will continue to increase. This paper studies on the analysis of amplification attack mechanisms in detail and proposes defense methodologies for scenarios where attackers, abused servers or victims are in a monitoring network.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.19-31
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• 2009

Network management involves configuring and operating various network elements in a suitable manner. Generally, a network management system can perform basic functionalities such as configuration management, performance management, and fault management. Due to the open structure of the Internet, the volume of network traffic and the network equipment used have increased in size and complexity. Therefore, it is expensive and time consuming to develop a network management program for heterogeneous network equipment in an SNMP.based network. In order to facilitate the management of network environments and the control of heterogeneous devices in an efficient manner, we propose an Active Network Management System (ANMS) comprising an automatic generator that uses triggers to generate a network management program. The concept of triggers can be represented through event condition action rules performed in response to a change in the status of a network environment. The proposed ANMS comprises basic components for real time network management and also includes an automatic generator (AG). When the ANMS is monitoring network elements that are newly added or changed, a trigger rule is activated and these components are then able to collaborate and automatically generate a new network management program by using the information provided along with the SNMP libraries. Our method is useful for expanding the network structure and replacing network equipment. Through experiments, we have proved that our ANMS is useful when new network objects are added or changed in the network environment to expand the network structure. Further, we have verified that our ANMS system reduces the time and cost required to develop a network management program as compared to the manual method used in existing network management systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.5
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• pp.72-84
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• 2017

Together with the Big Data of the 4th Industrial Revolution, the traffic information system has been changed to an section detection system by the point detection system. With DTG(Digital Tachograph) data based on Global Navigation Satellite System, the properties of raw data and data according to processing step were examined. We identified the vehicle trajectory, the link travel time of individual vehicle, and the link average travel time which are generated according to the processing step. In this paper, we proposed a application method for traffic management as characteristics of processing data. We selected the historical data considering the data management status of the center and the availability at the present time. We proposed a method to generate the Travel Time Index with historical link average travel time which can be collected all the time with wide range. We propose a method to monitor the traffic congestion using the Travel Time Index, and analyze the case of intersections when the traffic operation method changed. At the same time, the current situation which makes it difficult to fully utilize DTG data are suggested as limitations.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.4
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• pp.42-50
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• 2008

The real-time bio-signal monitoring system based on the ZigBee and SIP/RTP has proposed and implemented for telemedicine but that has some problems at the stabilities to transmit bio-signal from the sensors to the other sides. In this paper, we designed and implemented a real-time bio-signal monitoring system that is focused on the reliability and efficiency for transmitting bio-signal at real-time. We designed the system to have enhanced architecture and performance in the ubiquitous sensor network, SIP/RTP real-time transmission and management of the database. The Bluetooth network is combined with ZigBee network to distribute traffic of the ECG and the other bio-signal. The modified and multiplied RTP session is used to ensure real-time transmission of ECG, other bio-signals and speech information on the internet. The modified ECG compression method based on DWLT and MSVQ is used to reduce data rate for storing ECG to the database. Finally we implemented a system that has improved performance for transmitting bio-signal from the sensors to the monitoring console and database. This implemented system makes possible to make various applications to serve U-health care services.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.799-806
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• 2020

The rapid growth of smartphone-to-Internet of Things (IoT) connections and the explosive demand for data usage centered on mobile video are increasing day by day, and this increase in data usage creates many problems in the IP system. In a full-based environment, in which information requesters focus on information providers to receive information from specific servers, problems arise with bottlenecks and large data processing. To address this problem, CCN networking technology, a future network technology, has emerged as an alternative to CCN networking technology, which reduces bottlenecks that occur when requesting popular content through caching of intermediate nodes and increases network efficiency, and can be applied to military information and communication networks to address the problem of traffic concentration and the use of various surveillance equipment in full-based networks, such as scientific monitoring systems, and to provide more efficient content.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.794-804
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• 2018

Various applications running in computers exchange information in the form of packets through the network. Most packets are formatted into UDP/IP or TCP/IP standard. Network management administrators of enterprises and organizations should be able to monitor and manage packets transmitted over the network for Internet traffic measurement & monitoring, network security, and so on. The goal of this paper is to analyze the performance of several algorithms which closely examine and analyze payloads in a DPI(Deep Packet Inspection) system. The main procedure of packet payload analysis is to quickly search for a specific pattern in a payload. In this paper, we introduce several algorithms which detect a specific pattern in payloads, analyze the performance of them from three perspectives, and suggest an application method suitable for requirements of a given DPI system.

• Journal of the Korea Society for Simulation
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• v.23 no.1
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• pp.11-20
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• 2014

In this paper, we analyze the performance and reliability of the next-generation KRTCS (Korea Radio Train System) network. The KRTCS has been designed to manage and control the overall status of trains including location, speed, stop position, door open/close status and interior monitoring and so forth. System faults of the KRTCS operation can lead to the disruption of smooth train flow, even to terrible traffic accident. Prior to installation we need to assure the reliability of the designed KRTCS system. For this purpose, we simulated and analyzed the KRTCS network using QualNet simulator, assuming the various environmental operation data of train flows and communication faults that can be found in real telecommunication networks.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• Current Optics and Photonics
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• v.7 no.3
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• pp.237-247
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• 2023

In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.58-70
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• 2019

In the case of a pedestrian traffic accident, it has a large-scale danger directly connected by a fatal accident at the time of the accident. The domestic ITS is not used for intelligent risk classification because it is used only for collecting traffic information despite of the construction of good quality traffic infrastructure. The CNN based pedestrian detection classification model, which is a major component of the proposed system, is implemented on an embedded system assuming that it is installed and operated in a restricted environment. A new model was created by improving YOLO's artificial neural network, and the real-time detection speed result of average accuracy 86.29% and 21.1 fps was shown with 20,000 iterative learning. And we constructed a protocol interworking scenario and implementation of a system that can connect with the ITS. If a pedestrian accident prevention system connected with ITS will be implemented through this study, it will help to reduce the cost of constructing a new infrastructure and reduce the incidence of traffic accidents for pedestrians, and we can also reduce the cost for system monitoring.