• Journal of Information Processing Systems
• /
• v.19 no.1
• /
• pp.130-138
• /
• 2023

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

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

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1568-1570
• /
• 2005

The internet is already a part of life. It is very convenient and people can do almost everything with internet that should be done in real life. Along with the increase of the number of internet user, various network attacks through the internet have been increased as well. Also, Large-scale network attacks are a cause great concern for the computer security communication. These network attack becomes biggest threat could be down utility of network availability. Most of the techniques to detect and analyze abnormal traffic are statistic technique using mathematical modeling. It is difficult accurately to analyze abnormal traffic attack using mathematical modeling, but network simulation technique is possible to analyze and simulate under various network simulation environment with attack scenarios. This paper performs modeling and simulation under virtual network environment including $NGSS^{1}$ system to analyze abnormal traffic-flooding attack.

• Journal of KIISE:Information Networking
• /
• v.32 no.4
• /
• pp.433-442
• /
• 2005

An increase In traffic has a large Influence on the performance of a total network. Therefore, traffic management has become an important issue of network management. In this paper, we propose a new routing algorithm that attempts to analyze network conditions using time series prediction models and to propose predictive optimal routing decisions. Traffic congestion is assumed when the predicting result is bigger than the permitted bandwidth. By collecting traffic in real network, the predictable model is obtained when it minimizes statistical errors. In order to predict network traffic based on time series models, we assume that models satisfy a stationary assumption. The stationary assumption can be evaluated by using ACF(Auto Correlation Function) and PACF(Partial Auto Correlation Function). We can obtain the result of these two functions when it satisfies the stationary assumption. We modify routing oaths by predicting traffic in order to avoid traffic congestion through experiments. As a result, Predicting traffic and balancing load by modifying paths allows us to avoid path congestion and increase network performance.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.20 no.41
• /
• pp.103-112
• /
• 1997

In this paper, a nonhierarchial integrated information network with circuit switched and packet switched traffic is considered. It is assumed that circuit switched traffic is allowed to attempt an alternate path if the direct path is blocked, whereas packet switched traffic is not. The network uses a movable boundary scheme to allocate bandwidth dynamically. To analyze the performance of this type of network, EEBP(End to End Blocking Probability) is selected as a measure for circuit switched traffic and average time delay for packet switched traffic, respectively. EEBP and average time delay are derived analytically. Using the two proposed measure, the performance of the network under various bandwidth allocations and arrival patterns are observed. Moreover, the arrival rate of one link for circuit switched traffic is obtained from an approximation formula. Simulation results reveal the validity of the proposed approximation method.

• Journal of Korean Society of Transportation
• /
• v.14 no.3
• /
• pp.127-141
• /
• 1996

This paper presents a unique approach to urban traffic network signal control. This paper begins with an introduction to traffic control in general, and then goes on to describe the approach of fuzzy control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic network condition and adjacent intersection. The signal timing parameters evolve dynamically using only local information to improve traffic signal flow. The signal timing at an intersection is defined by three parameters : cycle time, phase split, off set. Fuzzy decision rules are used to adjust three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. In this paper the effectiveness of this method is showed through simulation of the traffic signal flow in a network of controlled intersection.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.2
• /
• pp.313-329
• /
• 2011

To provide a seamless network to customers, Internet service providers must promptly detect and control abnormal traffic. One approach is to shorten the traffic information measurement cycle. However, performance degradation is inevitable if traffic measurement servers merely shorten the cycle and measure all traffic. This paper presents a software architecture that can measure traffic more frequently without degrading performance by estimating the level of abnormal traffic. The algorithm in the architecture estimates the values of the interface group objects in MIB by using the IP group objects thereby reducing the number of measurements and the size of measured data. We evaluated this architecture on part of Internet service provider's IP network. When the traffic was measured 5 times more than before, the CPU usage and TPS of the proposed scheme was 7% and 41% less than that of the original scheme while the false positive rate and false negative rate were 3.2% and 2.7% respectively.

• Journal of Digital Convergence
• /
• v.10 no.1
• /
• pp.323-333
• /
• 2012

Network traffic demand is increasing explosively because of various smart equipment and services on smart era. It causes of traffic overload for wireless and wired network. Network management system is very important to control the explosion of data traffic. It uses SNMP to communicate with various network resources for management functions and creates lots of management traffic. Those are can be serious traffic congestion on a network. I propose an improving function of SNMP to minimize unnecessary traffics between manager and agent for collecting the Trend Analysis Information which is mainly used to monitor and accumulate for a specific time period in this paper. The results of test show it has compatibility with the existing SNMP and greatly decreases the amount of network traffic and response time.

• Journal of KIISE
• /
• v.43 no.11
• /
• pp.1223-1232
• /
• 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 the Korea Society for Simulation
• /
• v.18 no.1
• /
• pp.27-34
• /
• 2009

Currently network users, especially the number of internet users, increase rapidly. Also, high quality of service is required and this requirement results a sudden network traffic increment. As a result, an efficient management system for huge network traffic becomes an important issue. Ontology/data engineering based context awareness using the System Entity Structure (SES) concepts enables network administrators to access traffic data easily and efficiently. The network traffic analysis system, which is studied in this paper, is designed and implemented based on a model and simulation using data engineering methodology to be avaiable in evaluating large network traffic data. Extensible Markup Language (XML) is used for metadata language in this system. The information which is extracted from the network traffic analysis system could be modeled and simulated in Discrete Event Simulation (DEVS) methodology for further works such as post simulation evaluation, web services, and etc.