• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.12
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• pp.108-116
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• 2009

In this paper, we propose a Ethernet based audio distribution system with a new L1/L2 hybrid switching scheme, and evaluate its performance. The proposed scheme not only offers guaranteed low latency and jitter characteristics that are essentially required for the distribution of high-quality uncompressed audio traffic, and but also provide an efficient transmission of data traffic on the Ethernet environment. The audio distribution system with a proposed scheme consists of a master node and a number of relay nodes, and all nodes are mutually connected as a daisy-chain topology through up and downlinks. The master node generates an audio frame for each cycle of 125us, and the audio frame has 24 time slotted audio channels for carrying stereo 24 channels of 16-bit PCM sampled audio. On receiving the audio frame from its upstream node via the downlink, each intermediate node inserts its audio traffic to the reserved time slot for itself, then relays again to next node through its physical layer(L1) transmission - repeating. After reaching the end node, the audio frame is loopbacked through the uplink. On repeating through the uplink, each node makes a copy of audio slot that node has to receive, then play the audio. When the audio transmission is completed, each node works as a normal L2 switch, thus data frames are switched during the remaining period. For supporting this L1/L2 hybrid switching capability, we insert a glue logic for parsing and multiplexing audio and data frames at MII(Media Independent Interlace) between the physical and data link layers. The proposed scheme can provide a good delay performance and transmission efficiency than legacy Ethernet based audio distribution systems. For verifying the feasibility of the proposed L1/L2 hybrid switching scheme, we use OMNeT++ as a simulation tool with various parameters. From the simulation results, one can find that the proposed scheme can provides outstanding characteristics in terms of both jitter characteristic for audio traffic and transmission efficiency of data traffics.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.

• Journal of International Area Studies (JIAS)
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• v.14 no.1
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• pp.77-106
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• 2010

As for the matter of guardianship-benefit network which has been at the heart of the discussion of power elites and clan politics in Kazakhstan, it has been often maintained that it is basically formed by the framework of the regional and descent connection net called Zhuz or at least it has been heavily under Zhuz's influence. But it is pointed out that the controversy of Zhuz suffers from a lot of limitations in explaining the surface of power elites in the recent process of political changes and the rearrangement of power relations. Consequently, this paper tried to take a closer look at the matter focusing on the social backgrounds of elites from Junior zhuz, who have been estimated to be relatively pushed back in terms of the advancement into the central power. As a result, it was found that the backgrounds of clan and tribe origin within Zhuz couldn't have any foundation to be seen as a decisive element through which they could grow into power elites. The phenomenon of Kazakhstani elites is a legacy of concrete historic situations. The important consideration points for analyzing the emergence of elites which could be applied to a nomadic and traditional society can hardly be an invariable framework for analyzing modern elites since independence. Since 2000, Kazakhstan has experienced economic changes including privatization due to the absolute strengthening of presidential influence which turned into a foundation for a new authoritarian system, the rearrangement of the inner circle of power, and their decisions. These changes in situations have had profound effects on the character of power elites. The phenomenon that clandestine connections have shown their appearances as they have gotten intertwined with various factors, in particular, in the economic field which has been heavily under Junior zhuz makes us convinced that the elite organization in Kazakhstan has always been the product of political and economic changes. In reality, the behaviors of elites were the outcome continuously reflecting environmental situations surrounding them, and those situations lie in a complicated and multiple-layered connection net. Therefore, it is believed that having interests in elites' social backgrounds and maintaining many pieces of information on them will be able to be a more useful approach to analyzing the elite society in the future in that interests in their social backgrounds become an informant of various network formation nets which reflect real situations.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.