• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2B
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• pp.131-140
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• 2011

As the number of Internet users and applications is increasing, the importance of application traffic classification is growing more and more for efficient network management. While a number of methods for traffic classification have been introduced, such as signature-based and machine learning-based methods, Skype application, which uses encrypted communication on its own P2P network, is known as one of the most difficult traffic to identify. In this paper we propose a novel method to identify Skype application traffic on the fly. The main idea is to setup a list of Skype host information {IP, port} by examining the packets generated in the Skype login process and utilizes the list to identify other Skype traffic. By implementing the identification system and deploying it on our campus network, we proved the performance and feasibility of the proposed method.

• Journal of KIISE
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• v.43 no.11
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• pp.1223-1232
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• 2016

A virtual network technology can provide a network reflecting the requirements of various services. The virtual network can distribute resources of the physical network to each virtual slice. An efficient resource distribution technique is needed to reflect the requirements of various services. Existing bandwidth distribution techniques can only control downlink traffic without taking traffic conditions on the network into account. Downlink and uplink share the same resources in a wireless network. The existing bandwidth distribution techniques assumed that all stations generate saturated traffic. Therefore, the existing bandwidth distribution technique cannot make traffic isolation in a virtual wireless network. In this paper, we proposed a traffic-based bandwidth control techniques to solve these problems. We applied Software-Defined Networking(SDN) to the virtual wireless network, monitored the traffic at each station, and searched for stations that generated unsaturated traffic. We also controlled both uplink and downlink traffics dynamically based on monitoring information. Our system can be implemented with legasy 802.11 clients and SDN-enabled APs. After the actual test bed configuration, it was compared to existing techniques. As a result, the distribution performance of the proposed technique was improved by 14% in maximum.

• Journal of KIISE
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• v.41 no.9
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• pp.617-624
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• 2014

Malicious traffic, such as DDoS attack and botnet communications, refers to traffic that is generated for the purpose of disturbing internet networks or harming certain networks, servers, or hosts. As malicious traffic has been constantly evolving in terms of both quality and quantity, there have been many researches fighting against it. In this paper, we propose an effective malicious traffic detection method that exploits the X-means clustering algorithm. We also suggest how to analyze statistical characteristics of malicious traffic and to define metrics that are used when clustering. Finally, we verify effectiveness of our method by experiments with two released traffic data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.687-708
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• 2017

The machine-to-machine (M2M) communication is featured by tremendous number of devices, small data transmission, and large uplink to downlink traffic ratio. The massive access requests generated by M2M devices would result in the current medium access control (MAC) protocol in LTE/LTE-A networks suffering from physical random access channel (PRACH) overload, high signaling overhead, and resource underutilization. As such, fairness should be carefully considered when M2M traffic coexists with human-to-human (H2H) traffic. To tackle these problems, we propose an adaptive Slotted ALOHA (S-ALOHA) and time division multiple access (TDMA) hybrid protocol. In particular, the proposed hybrid protocol divides the reserved uplink resource blocks (RBs) in a transmission cycle into the S-ALOHA part for M2M traffic with small-size packets and the TDMA part for H2H traffic with large-size packets. Adaptive resource allocation and access class barring (ACB) are exploited and optimized to maximize the channel utility with fairness constraint. Moreover, an upper performance bound for the proposed hybrid protocol is provided by performing the system equilibrium analysis. Simulation results demonstrate that, compared with pure S-ALOHA and pure TDMA protocol under a target fairness constraint of 0.9, our proposed hybrid protocol can improve the capacity by at least 9.44% when ${\lambda}_1:{\lambda}_2=1:1$and by at least 20.53% when ${\lambda}_1:{\lambda}_2=10:1$, where ${\lambda}_1,{\lambda}_2$ are traffic arrival rates of M2M and H2H traffic, respectively.

• Journal of Communications and Networks
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• v.14 no.6
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• pp.653-661
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• 2012

High-availability seamless redundancy (HSR) is a redundancy protocol for Ethernet networks that provides two frame copies for each frame sent. Each copy will pass through separate physical paths, pursuing zero fault recovery time. This means that even in the case of a node or a link failure, there is no stoppage of network operations whatsoever. HSR is a potential candidate for the communications of a smart grid, but its main drawback is the unnecessary traffic created due to the duplicated copies of each sent frame, which are generated and circulated inside the network. This downside will degrade network performance and might cause network congestion or even stoppage. In this paper, we present two approaches to solve the above-mentioned problem. The first approach is called quick removing (QR), and is suited to ring or connected ring topologies. The idea is to remove the duplicated frame copies from the network when all the nodes have received one copy of the sent frame and begin to receive the second copy. Therefore, the forwarding of those frame copies until they reach the source node, as occurs in standard HSR, is not needed in QR. Our example shows a traffic reduction of 37.5%compared to the standard HSR protocol. The second approach is called the virtual ring (VRing), which divides any closed-loop HSR network into several VRings. Each VRing will circulate the traffic of a corresponding group of nodes within it. Therefore, the traffic in that group will not affect any of the other network links or nodes, which results in an enhancement of traffic performance. For our sample network, the VRing approach shows a network traffic reduction in the range of 67.7 to 48.4%in a healthy network case and 89.7 to 44.8%in a faulty network case, compared to standard HSR.

• Journal of Korean Society of Transportation
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• v.22 no.5
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• pp.99-109
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• 2004

Most traffic congestion on a freeway occurs in the merge area, where conflicts between mainline traffic and on-ramp traffic are frequently generated. So far, research on the merge area has mainly dealt with free flow traffic and research on the congested traffic at the merge area is rare. This study investigates the relationships between mainline traffic and on-ramp traffic at three different segments of the merge area. For this purpose, new indicators based on such traffic variables as flow, speed, and density are used. The results show that a negative relationship exists between mainline and on-ramp flow. It is also found that the speed and the density of the right two lanes in the mainline traffic are significantly affected by the on-ramp flow. Based on the correlation analysis of the indicators, it is confirmed that the ramp influence area is the right two lanes of the freeway mainline. The revealed relationships between mainline and on-ramp traffic may help to analyze the capacity of the downstream freeway segment of the merging area in congested traffic. The findings of this studyalso provide a basis to develop a model that estimates the merge traffic volume in congested traffic, which is neither theoretically nor empirically sound in most other traffic flow models developed so far.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.100-112
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• 2021

With the advent of big data, traffic prediction has been developed based on historical data analysis methods, but this method deteriorates prediction performance when a traffic incident that has not been observed occurs. This study proposes a method that can compensate for the reduction in traffic prediction accuracy in traffic incidents situations by hybrid approach of machine learning and traffic simulation. The blind spots of the data-driven method are revealed when data patterns that have not been observed in the past are recognized. In this study, we tried to solve the problem by reinforcing historical data using traffic simulation. The proposed method performs machine learning-based traffic prediction and periodically compares the prediction result with real time traffic data to determine whether an incident occurs. When an incident is recognized, prediction is performed using the synthetic traffic data generated through simulation. The method proposed in this study was tested on an actual road section, and as a result of the experiment, it was confirmed that the error in predicting traffic state in incident situations was significantly reduced. The proposed traffic prediction method is expected to become a cornerstone for the advancement of traffic prediction.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2010.04a
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• pp.223-224
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• 2010

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.3
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• pp.229-235
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• 2011

Traffic noise pollution is a growing problem that highly affects the health of people in urban areas. In order to implement effective measures against traffic noise the noise mapping about its distribution is imperative. Urban traffic noise maps will help to establish the existing baseline so that we will be able to look for the effective way to control the noise. They will also let us see in an understandable and visual way how noise spreads from roads into residential areas. In this paper we considered three development types between roads and residential areas, and applied the interpolation of ArcGIS for noise attenuation with distance from the roads to find the noise level at the parcels positions in residential areas, and then generated the noise map using the interpolated results. Therefore we present results that the traffic noise level within residential areas exceed the national noise standard and also can estimate the noise level of individual parcels. These results can be used for traffic noise control planning or assessment of officially assessed land price in near future.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.177-186
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• 2008

Video Image Detection System can be used for various traffic managements including traffic operation and traffic safety. Video Image Detection Technique can be divide by Tripwire System and Tracking System. Autoscope, which is widely used in the market, utilizes the Tripwire System. In this study, we developed an individual vehicle tracking system that can collect microscopic traffic information and also developed another image detection technology under the Tripwire System. To prove the accuracy and reliability of the newly developed systems, we compared the traffic data of the systems with those generated by Autoscope. The results showed that 0.35% of errors compared with the real traffic counts and 1.78% of errors with Autoscope. Performance comparisons on speed from the two systems showed the maximum errors of 1.77% with Autoscope, which confirms the usefulness of the newly developed systems.