• Journal of the Korea Society for Simulation
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• v.21 no.3
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• pp.17-24
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• 2012

This paper suggests Effectiveness Evaluation Methods of DDoS attacks countermeasures model by simulation. According to the security objectives that are suggested by NIST(National Institute of Standards and Technology), It represents a hierarchical Effectiveness Evaluation Model. we calculated the weights of factors that security objectives, security controls, performance indicator through AHP(Analytic Hierarchy Process) analysis. Subsequently, we implemented Arena Simulation Model for the calculation of function points at the performance indicator. The detection and protection algorithm involve methods of critical-level setting, signature and anomaly(statistic) based detection techniques for Network Layer 4, 7 attacks. Proposed Effectiveness Evaluation Model can be diversely used to evaluate effectiveness of countermeasures and techniques for new security threats each organization.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.603-615
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• 2005

As recent business environments are changed and become complex, a more efficient and effective business process management are needed. This paper proposes a new approach to the automatic execution of business processes using Event-Condition-Action (ECA) rules that can be automatically triggered by an active database. First of all, we propose the concept of blocks that can classify process flows into several patterns. A block is a minimal unit that can specify the behaviors represented in a process model. An algorithm is developed to detect blocks from a process definition network and transform it into a hierarchical tree model. The behaviors in each block type are modeled using ACTA formalism. This provides a theoretical basis from which ECA rules are identified. The proposed ECA rule-based approach shows that it is possible to execute the workflow using the active capability of database without users' intervention.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.159-174
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• 2015

Dragomiretskiy and Zosso (2014) developed a new decomposition method, termed variational mode decomposition (VMD), which is efficient for handling the tone detection and separation of signals. However, VMD may be inefficient in the presence of missing data since it is based on a fast Fourier transform (FFT) algorithm. To overcome this problem, we propose a new approach based on a novel combination of VMD and hierarchical (or h)-likelihood method. The h-likelihood provides an effective imputation methodology for missing data when VMD decomposes the signal into several meaningful modes. A simulation study and real data analysis demonstrates that the proposed method can produce substantially effective results.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.544-549
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• 2006

This research is to introduce about Judgment System for Intelligent Movement(JSIM) that can perform assistance work of human brain. JSIM can order autonomous command and also it can be directly controlled by user. This research assumes that control object is limited to Mobile Robot(MR) Mobile robot offers image and ultrasonic sensor information to user carrying JSIM and it performs guide to user. JSIM having PDA and Sensor-box controls velocity and direction of the mobile robot by soft-computing method that inputs user's command and information that is obtained to mobile robot. Also it controls mobile robot to achieve various movement. This paper introduces wearable JSIM that communicates with around devices and that can do intelligent judgment. To verify the possibility of the proposed system, in real environment, the simulation of control and application problem lot mobile robot will be introduced. Intelligent algorithm in the proposed system is generated by mixed hierarchical fuzzy and neural network.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.329-339
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• 2003

In this study, an approach of image fusion in decision level has been proposed for unsupervised image classification using the images acquired from multiple sensors with different characteristics. The proposed method applies separately for each sensor the unsupervised image classification scheme based on spatial region growing segmentation, which makes use of hierarchical clustering, and computes iteratively the maximum likelihood estimates of fuzzy class vectors for the segmented regions by EM(expected maximization) algorithm. The fuzzy class vector is considered as an indicator vector whose elements represent the probabilities that the region belongs to the classes existed. Then, it combines the classification results of each sensor using the fuzzy class vectors. This approach does not require such a high precision in spatial coregistration between the images of different sensors as the image fusion scheme of pixel level does. In this study, the proposed method has been applied to multispectral SPOT and AIRSAR data observed over north-eastern area of Jeollabuk-do, and the experimental results show that it provides more correct information for the classification than the scheme using an augmented vector technique, which is the most conventional approach of image fusion in pixel level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2964-2985
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• 2019

Global data center IP traffic is expected to reach 20.6 zettabytes (ZB) by the end of 2021. Intra-data center networks (Intra-DCN) will account for 71.5% of the data center traffic flow and will be the largest portion of the traffic. The understanding of traffic distribution in IntraDCN is still sketchy. It causes significant amount of bandwidth to go unutilized, and creates avoidable choke points. Conventional transport protocols such as Optical Packet Switching (OPS) and Optical Burst Switching (OBS) allow a one-sided view of the traffic flow in the network. This therefore causes disjointed and uncoordinated decision-making at each node. For effective resource planning, there is the need to consider joining the distributed with centralized management which anticipates the system's needs and regulates the entire network. Methods derived from Kalman filters have proved effective in planning road networks. Considering the network available bandwidth as data transport highways, we propose an intelligent enhanced SDN concept applied to OBS architecture. A management plane (MP) is added to conventional control (CP) and data planes (DP). The MP assembles the traffic spatio-temporal parameters from ingress nodes, uses Kalman filtering prediction-based algorithm to estimate traffic demand. Prior to packets arrival at edges nodes, it regularly forwards updates of resources allocation to CPs. Simulations were done on a hybrid scheme (1+1) and on the centralized OBS. The results demonstrated that the proposition decreases the packet loss ratio. It also improves network latency and throughput-up to 84 and 51%, respectively, versus the traditional scheme.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.886-895
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• 2018

In this paper, we introduce a technique to detect the video forgery by using the regularity that occurs in the video compression process. The proposed method uses the hierarchical regularity lost by the video double compression and the frame deletion. In order to extract such irregularities, the depth information of CU and TU, which are basic units of HEVC, is used. For improving performance, we make a depth map of CU and TU using local information, and then create input data by grouping them in GoP units. We made a decision whether or not the video is double-compressed and forged by using a general three-dimensional convolutional neural network. Experimental results show that it is more effective to detect whether or not the video is forged compared with the results using the existing machine learning algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.1-7
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• 2019

Recently, multimedia streaming service has been activated, and the demand for high-quality multimedia convergence contents services is predicted to increase significantly in the future. The issues of the increasing network load due to the rise of multimedia streaming traffic must be addressed in order to provide QoS guaranteed services. To do this, an efficient network resource management and mobility support technologies are needed through seamless mobility support for heterogeneous networks. Therefore, in this paper, an MIH technology was used to recognize the network situation information in advance and reduce packet loss due to handover delays, and an ACLMIH-FHPMIPv6 is designed that can provide an intelligent interface through introducing a hierarchical mobility management technique in FPMIPv6 integrated network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1435-1440
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• 2021

With the advent of the low interest rate era, many investors are flocking to the stock market. In the past stock market, people invested in stocks labor-intensively through company analysis and their own investment techniques. However, in recent years, stock investment using artificial intelligence and data has been widely used. The success rate of stock prediction through artificial intelligence is currently not high, so various artificial intelligence models are trying to increase the stock prediction rate. In this study, we will look at various artificial intelligence models and examine the pros and cons and prediction rates between each model. This study investigated as stock prediction programs using artificial intelligence artificial neural network (ANN), deep learning or hierarchical learning (DNN), k-nearest neighbor algorithm(k-NN), convolutional neural network (CNN), recurrent neural network (RNN), and LSTMs.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.135-142
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• 2009

Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. The major techniques used in data mining are mining association rules, classification and clustering. Since these techniques are used individually, it is necessary to develop the methodology for rule extraction using a process of integrating these techniques. Rule extraction techniques assist humans in analyzing of large data sets and to turn the meaningful information contained in the data sets into successful decision making. This paper proposes an autonomous method of rule extraction using clustering and rough set theory. The experiments are carried out on data sets of UCI KDD archive and present decision rules from the proposed method. These rules can be successfully used for making decisions.