• Journal of Communications and Networks
• /
• v.14 no.3
• /
• pp.310-318
• /
• 2012

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.12 no.3
• /
• pp.149-153
• /
• 2012

We propose a traffic information service for which traffic data are collected over ad-hoc networks from neighbor vehicles, processed to minimize the data size, and eventually provided to its destination. The proposed scheme simply relies on the existing navigtion systems in vehicles and wireless communication devices for vehicle-to-vehicle communication, rather than on a separately established server. It allows collecting and analyzing traffic status of large areas without incorporating separated monitoring systems, e.g., probe cars and enables to provide accurate traffic information to drivers in timely manner. We also evaluate its performance by ns-3 simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.4 no.5
• /
• pp.859-876
• /
• 2010

In this paper, we propose a hierarchical application traffic classification system as an alternative means to overcome the limitations of the port number and payload based methodologies, which are traditionally considered traffic classification methods. The proposed system is a new classification model that hierarchically combines a binary classifier SVM and Support Vector Data Descriptions (SVDDs). The proposed system selects an optimal attribute subset from the bi-directional traffic flows generated by our traffic analysis system (KU-MON) that enables real-time collection and analysis of campus traffic. The system is composed of three layers: The first layer is a binary classifier SVM that performs rapid classification between P2P and non-P2P traffic. The second layer classifies P2P traffic into file-sharing, messenger and TV, based on three SVDDs. The third layer performs specialized classification of all individual application traffic types. Since the proposed system enables both coarse- and fine-grained classification, it can guarantee efficient resource management, such as a stable network environment, seamless bandwidth guarantee and appropriate QoS. Moreover, even when a new application emerges, it can be easily adapted for incremental updating and scaling. Only additional training for the new part of the application traffic is needed instead of retraining the entire system. The performance of the proposed system is validated via experiments which confirm that its recall and precision measures are satisfactory.

• Journal of Communications and Networks
• /
• v.7 no.2
• /
• pp.192-206
• /
• 2005

Integration of mobile networks and Internet has started with 2.5 generation of mobile cellular networks. Internet traffic is today dominant traffic type worldwide. The hanger for higher data rates needed for data traffic and new IP based services is essential in the development of future wireless networks. In such situation, even 3G with up to 2 Mbit/s has not provided data rates that are used by Internet users with fixed broadband dial-up or through wired local area networks. The solution to provide higher bit rates in wireless access network has been found in wireless LAN although initially it has been developed to extend wired LAN into wireless domain. In this paper, we propose and describe a solution created for interoperability between mobile cellular network and WLAN. The integration between two networks, cellular and WLAN, is performed on the authentication, authorization, and accounting, i.e., AAA side. For that purpose we developed WLAN access controller and WLAN AAA gateway, which provide gateway-type access control as well as charging and billing functionalities for the WLAN service. In the development process of these elements, we have considered current development stadium of all needed network entities and protocols. The provided solution provides cost-effective and easy-to-deploy PLMN-WLAN Internetworking scenario.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.1
• /
• pp.85-96
• /
• 2023

The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.

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

• The KIPS Transactions:PartB
• /
• v.12B no.6 s.102
• /
• pp.685-690
• /
• 2005

Recently, studies for effective multimedia data delivery have been widely performed. Among those researches, internet broadcasting is a technology to transmit various multimedia contents to variety of costumers on the internet simultaneously. To deliver large scale multimedia data such as internet broadcasting, efficient data delivery method is required. Therefore, technologies based on the overlay multicast of application larger multicast application are actively studied as an alternative for multicast overcoming shortcoming of uni-cast based technologies, which we one to one transmission methods. In this paper, we classify and study on the overlay multicasting for internet broadcasting, which can be implemented by application programs without modification in physical layer of the internet.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.24 no.10A
• /
• pp.1501-1506
• /
• 1999

DSRC(Dedicated Short Range Communications), which is a radio mobile communication protocol in ITS, is suited for traffic related information services. But in WAC(Wide-Area wireless communications), we could consider a conventional communication infrastructure in the case of supporting other applications besides ITS. Especially considering the current the trends to introduce wireless internet service into the mobile communication network, wireless internet service in ITS system is valuable. Although ITS LLC(Logical Link Control) protocols recommended by Europe and Japan are effective for traffic related services, it is not suitable for services related with geographical information, image or internet. That is because the data traffic characteristics is changed according to service types. Therefore we suggest a logical link control algorithm effective for traffic information related services as well as internet web service, and analyze its performance. As a result of numerical analysis and simulation, proposed algorithm shows larger performance improvements. For traffic information related services, the performance of DSRC LLC recommended by Europe and Japan is the same as that of proposed LLC protocol. However, for wireless internet web service, the performance of proposed protocol is much better than that of LLC protocol used in Europe and Japan.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.4
• /
• pp.678-691
• /
• 2015

Internet and mobile traffic continues to surge exponentially in recent years due to popularization of smart devices, the appearance of various internet services carrying large amount of traffic from richer content and applications. This phenomenon leaded to various network problems such as the congestion delay, the non-balanced traffic ratio between ISPs, the continuous network investment cost and the Internet access problems. In light of changed data-driven communication ecosystem, There are growing concerns by both academia and industry that settlement-free peering and full transit regime have the limitations such as not only difficulties in maintaining mutual benefits but also difficulties in securing investment incentives for upgrading network performance and quality. Thus, it becomes more necessary for introducing the evolved internet interconnection regime which can fulfill the All-IP network environment. This study derives core issues regarding internet interconnection regime in Korea and suggest new evolved alternatives based on three point of view(traffic optimization, cost optimization, network investment optimization) through the empirical analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.9
• /
• pp.3598-3614
• /
• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.