• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.51-58
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• 2016

The unprecedented power of cloud computing (CC) that enables free sharing of confidential data records for further analysis and mining has prompted various security threats. Thus, supreme cyberspace security and mitigation against adversaries attack during data mining became inevitable. So, privacy preserving data mining is emerged as a precise and efficient solution, where various algorithms are developed to anonymize the data to be mined. Despite the wide use of generalized K-anonymizing approach its protection and truthfulness potency remains limited to tiny output space with unacceptable utility loss. By combining L-diversity and (${\alpha}$,k)-anonymity, we proposed a hybrid K-anonymity data relocation algorithm to surmount such limitation. The data relocation being a tradeoff between trustfulness and utility acted as a control input parameter. The performance of each K-anonymity's iteration is measured for data relocation. Data rows are changed into small groups of indistinguishable tuples to create anonymizations of finer granularity with assured privacy standard. Experimental results demonstrated considerable utility enhancement for relatively small number of group relocations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3182-3202
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• 2015

Flexibly expanding the storage capacity required to process a large amount of rapidly increasing unstructured log data is difficult in a conventional computing environment. In addition, implementing a log processing system providing features that categorize and analyze unstructured log data is extremely difficult. To overcome such limitations, we propose and design a MongoDB-based unstructured log processing system (MdbULPS) for collecting, categorizing, and analyzing log data generated from banks. The proposed system includes a Hadoop-based analysis module for reliable parallel-distributed processing of massive log data. Furthermore, because the Hadoop distributed file system (HDFS) stores data by generating replicas of collected log data in block units, the proposed system offers automatic system recovery against system failures and data loss. Finally, by establishing a distributed database using the NoSQL-based MongoDB, the proposed system provides methods of effectively processing unstructured log data. To evaluate the proposed system, we conducted three different performance tests on a local test bed including twelve nodes: comparing our system with a MySQL-based approach, comparing it with an Hbase-based approach, and changing the chunk size option. From the experiments, we found that our system showed better performance in processing unstructured log data.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.436-443
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• 2011

Recognition of structures in urban environments is a fundamental ability for unmanned ground vehicles. In this paper we propose the geometrical featured voxel which has not only 3-D coordinates but also the type of geometrical properties of point cloud. Instead of dealing with a huge amount of point cloud collected by range sensors in urban, the proposed voxel can efficiently represent and save 3-D urban structures without loss of geometrical properties. We also provide an urban structure classification algorithm by using the proposed voxel and machine learning techniques. The proposed method enables to recognize urban environments around unmanned ground vehicles quickly. In order to evaluate an ability of the proposed map representation and the urban structure classification algorithm, our vehicle equipped with the sensor system collected range data and pose data in campus and experimental results have been shown in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3030-3049
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• 2023

With the development and wide application of hybrid network computing modes like cloud computing, edge computing and fog computing, the customer service requests and the collaborative optimization of various computing resources face huge challenges. Considering the characteristics of network environment resources, the optimized deployment of service resources is a feasible solution. So, in this paper, the optimal goals for deploying service resources are customer experience and service cost. The focus is on the system impact of deploying services on load, fault tolerance, service cost, and quality of service (QoS). Therefore, the alternate node filtering algorithm (ANF) and the adjustment factor of cost matrix are proposed in this paper to enhance the system service performance without changing the minimum total service cost, and corresponding theoretical proof has been provided. In addition, for improving the fault tolerance of system, the alternate node preference factor and algorithm (ANP) are presented, which can effectively reduce the probability of data copy loss, based on which an improved cost-efficient replica deployment strategy named ICERD is given. Finally, by simulating the random occurrence of cloud node failures in the experiments and comparing the ICERD strategy with representative strategies, it has been validated that the ICERD strategy proposed in this paper not only effectively reduces customer access latency, meets customers' QoS requests, and improves system service quality, but also maintains the load balancing of the entire system, reduces service cost, enhances system fault tolerance, which further confirm the effectiveness and reliability of the ICERD strategy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.921-928
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• 2016

Over the past few years, Smishing attacks such as malicious url and malicious application have been emerged as a major problem in South Korea since it caused big problems such as leakage of personal information and financial loss. Users are susceptible to Smishing attacks due to the fact that text message may contain curios content. Because of that reason, user could follow the url, download and install malicious APK file without any doubt or verification process. However currently Anti-Smishing App that adopted post-processing method is difficult to respond quickly. Users need a system that can determine whether the modification of the APK file and malicious url in real time because the Smishing can cause financial damage. This paper present the cloud-based system for verifying malicious url and malicious APK file in user device to prevent secondary damage such as smishing attacks and privacy information leakage.

• Journal of the Korean Geotechnical Society
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• v.31 no.11
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• pp.49-60
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• 2015

Open-water sediment disposal has many applications in costal construction. Dumping of sediment in open water can be divided into descending stage under water and sedimentation stage on the seabed, and retained rate is evaluated from analyzed results of these two successive stages. Descending particle cloud have two distinct thermal and swam phase, and trajectory equations for each phase are derived to describe settling velocity and radius of particle cloud. For sedimentation stage, a numerical simulator is used to calculate growth factors for particle fiction angle and current velocity. Retained rate is defined as a mass rate of remained sediment inside the circle which has a center at dumping point on the sea level and user-defined effective radius. Retained rate map for Singapore coast is presented with water depth of 20 m, current velocity of 0.0~1.5 m/s, and effective radius of 5 m. It will decrease sediment mass loss during disposal operation and minimize surrounding environmental pollution.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.43.1-43.1
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• 2018

Star formation in galaxies predominantly takes place in giant molecular clouds (GMCs). While it is widely believed that UV radiation feedback from young massive stars can destroy natal GMCs by exciting HII regions and driving their expansion, our understanding on how this actually occurs remains incomplete. To quantitatively assess the effect of UV radiation feedback on cloud disruption, we conduct a series of theoretical studies on the dynamics of HII regions and its role in controlling the star formation efficiency (SFE) and lifetime of GMCs in a wide range of star-forming environments. We first develop a semi-analytic model for the expansion of spherical dusty HII regions driven by the combination of gas and radiation pressures, finding that GMCs in normal disk galaxies are destroyed by gas-pressure driven expansion with SFE < 10%, while more dense and massive clouds with higher SFE are disrupted primarily by radiation pressure. Next, we turn to radiation hydrodynamic simulations of GMC dispersal to allow for self-consistent star formation as well as inhomogeneous density and velocity structures arising from supersonic turbulence. For this, we develop an efficient parallel algorithm for ray tracing method, which enables us to probe a range of cloud masses and sizes. Our parameter study shows that the net SFE, lifetime (measured in units of free-fall time), and the importance of radiation pressure (relative to photoionization) increase primarily with the initial surface density of the cloud. Unlike in the idealized spherical model, we find that the dominant mass loss mechanism is photoevaporation rather than dynamical ejection and that a significant fraction of radiation escapes through low optical-depth channels. We will discuss the astronomical.

• Journal of the Korean Institute of Gas
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• v.12 no.1
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• pp.42-47
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• 2008

The demand of gas as an eco-friendly energy source has being increased. With the demand of gas, the use of gas is also increased, so injury and loss of life by the explosion and fire have been increasing every year. Hence the influence on over-pressure caused by Vapor Cloud Explosion in enclosure of experimental booth was calculated by using the Hopkinson's scaling law and damage effect by the accident to a human body was estimated by applying the probit model. As a result of the damage estimation conducted by using the probit model, both the damage possibility of explosion overpressure to human over 3 meters away and that of overpressure to tympanum rupture over 25 meters away from the explosion shows nothing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5842-5861
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• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.

• Journal of Convergence for Information Technology
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• v.10 no.7
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• pp.7-13
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• 2020

IoT which is the core IT of the 4^th industrial revolution, is providing various services from users in the conversion with other industries. The IoT convergence technology is leading the communication paradigm of communication environment in accordance with the increase of convenience for users. However, it is urgently needed to establish the security measures for the rapidly-developing IoT convergence technology. As IoT is closely related to digital ethics and personal information protection, other industries should establish the measures for coping with threatening elements in accordance with the introduction of IoT. In case when security incidents occur, there could be diverse problems such as information leakage, damage to image, monetary loss, and casualty. Thus, this paper suggests a certificate management technique for safe control over access in IoT-based Cloud convergence environment. This thesis designed the device/user registration, message communication protocol, and device renewal/management technique. On top of performing the analysis on safety in accordance with attack technique and vulnerability, in the results of conducting the evaluation of efficiency compared to the existing PKI-based certificate management technique, it showed about 32% decreased value.