• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.315-319
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• 2022

Cloud computing has emerged to be the most effective headway for investigating crime especially cybercrime in this modern world. Even as we move towards an information technology-controlled world, it is important to note that when innovations are made, some negative implications also come with it, and an example of this is these criminal activities that involve technology, network devices, and networking that have emerged as a result of web improvements. These criminal activities are the ones that have been termed cybercrime. It is because of these increased criminal activities that organizations have come up with different strategies that they use to counter these crimes, and one of them is carrying out investigations using the cloud environment. A cloud environment has been defined as the use of web-based applications that are used for software installation and data stored in computers. This paper examines problems that are a result of cybercrime investigation in the cloud environment. Through analysis of the two components in play; cybercrime and cloud environment, we will be able to understand what are the problems that are encountered when carrying out investigations in cloud forensics. Through the use of secondary research, this paper found out that most problems are associated with technical and legal channels that are involved in carrying out these investigations. Investigator's mistakes when extracting pieces of evidence form the most crucial problems that take a lead when it comes to cybercrime investigation in the cloud environment. This paper not only flags out the challenges that are associated with cybercrime investigation in cloud environments but also offer recommendations and suggested solutions that can be used to counter the problems in question here. Through a proposed model to perform forensics investigations, this paper discusses new methodologies solutions, and developments for performing cybercrime investigations in the cloud environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.5
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• pp.69-79
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• 2010

In the current cloud environments, the applications are executed on the remote cloud server, and they also utilize computing resources of the remote cloud server such as physical memory and CPU. Therefore, if remote server is exposed to security threat, every applications in remote server can be victim by several security-attacks. Especially, despite many advantages, both individuals and businesses often have trouble to start the cloud services according to the malicious administrator of the cloud server. We propose a security-enhanced local process executing scheme resolving vulnerability of current cloud computing environments. Since secret data is stored in the local, we can protect secret data from security threats of the cloud server. By utilizing computing resource of local computer instead of remote server, high-secure processes can be set free from vulnerability of remote server.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.337-346
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• 2012

In the view from most application system developers and users, cloud computing becomes popular in recent years and is still evolving. But in fact it is not easy to reach at the level of actual operations. Despite, it is known that the cloud in the practical stage provides a new pattern for deploying a geo-spatial application. However, domestically geo-spatial application implementation and operation based on this concept or scheme is on the beginning stage. It is the motivation of this works. Although this study is an introductory level, a simple and practical processed result was presented. This study was carried out on Amazon web services platform, as infrastructure as a service in the geo-spatial areas. Under this environment, cloud instance, a web and mobile system being previously implemented in the multi-layered structure for geo-spatial open sources of database and application server, was generated. Judging from this example, it is highly possible that cloud services with the functions of geo-processing service and large volume data handling are the crucial point, leading a new business model for civilian remote sensing application and geo-spatial enterprise industry. The further works to extend geo-spatial applications in cloud computing paradigm are left.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.339-350
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• 2017

In recent, cloud computing paradigm and open source as a huge trend in the Information Communication Technology (ICT) are widely applied, being closely interrelated to each other in the various applications. The integrated services by both technologies is generally regarded as one of a prospective web-based business models impacting the concerned industries. In spite of progressing those technologies, there are a few application cases in the geo-based application domains. The purpose of this study is to develop a cloud-based service system for satellite image processing based on the pure and full open source. On the OpenStack, cloud computing open source, virtual servers for system management by open source stack and image processing functionalities provided by OTB have been built or constructed. In this stage, practical image processing functions for KOMPSAT within this service system are thresholding segmentation, pan-sharpening with multi-resolution image sets, change detection with paired image sets. This is the first case in which a government-supporting space science institution provides cloud-based services for satellite image processing functionalities based on pure open source stack. It is expected that this implemented system can expand with further image processing algorithms using public and open data sets.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.57-68
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• 2012

The recent proliferation of various smart devices like the smartphone, tablet PC, and smart TV enables consumers to download various applications from the network and to access private files stored in their desktop server at any time and at any place. The trend of ubiquitous access seems to have become stronger and more diversified toward a ubiquitous network computing environment with the aggressive deployment of commercial cloud services. Recently, many Korean network service providers launched commercial B2C public cloud services, which were widely adopted by smart device users. They include Daum cloud, N drive, ucloud, and uplus box, mostly provided by major Korean telecom companies and portals. This paper aims to explore consumers' adoption behaviors toward the B2C public cloud services that were recently deployed in the Korean market. In order to achieve the goal, we identified key influencing factors that affect the consumers' adoption behaviors, based on an extension of the technology acceptance model (TAM). Several hundred smart device users were surveyed to test the generic regression model with the extended set of TAM variables.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2815-2839
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• 2020

In a cloud computing paradigm, allocation of various virtualized ICT resources is a complex problem due to the presence of heterogeneous application (MapReduce, content delivery and networks web applications) workloads having contentious allocation requirements in terms of ICT resource capacities (resource utilization, execution time, response time, etc.). This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. However, there is no published research available in this domain that clearly address this research problem and provides research taxonomy for classification of resource allocation techniques including strategic, target resources, optimization, scheduling and power. Hence, the main aim of this paper is to identify open challenges faced by the cloud service provider related to allocation of resource such as servers, storage and networks in cloud computing. More than 70 articles, between year 2007 and 2020, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and are reviewed under clearly defined objectives. Lastly, the evolution of research in resource allocation techniques has also been discussed along with salient future directions in this area.

• Journal of the Optical Society of Korea
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• v.18 no.5
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• pp.605-610
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• 2014

Clouds play an important role in climate change, in the prediction of local weather, and also in aviation safety when instrument assisted flying is unavailable. Presently, various ground-based instruments used for the measurements of the cloud base height or velocity. Lidar techniques are powerful and have many applications in climate studies, including the clouds' temperature measurement, the aerosol particle properties, etc. Otherwise, it is very circumscribed in cloud velocity measurements because there is no Doppler effect if the clouds move in the perpendicular direction to the laser beam path of Doppler lidar. In this paper, we present a method for the measurement of cloud velocity using lidar's range detection and DIC (Digital Image Correlation) system to overcome the disadvantage of Doppler lidar. The lidar system acquires the distance to the cloud, and the cloud images are tracked using the developed fast correlation algorithm of DIC. We acquired the velocities of clouds using the calculated distance and DIC algorithm. The measurement values had a linear distribution.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.137-141
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• 2015

Mobile cloud computing uses cloud service to solve the resource constraint problem of mobile devices. Offloading means that a task executed on the mobile device commits to cloud and many studies related to the energy consumption have been researched. In this paper, we designed a response time model considering sensing data synchronization to estimate the efficiency of the offloading scheme in terms of the response time. The proposed model considers synchronization of required sensing data to improve the accuracy of response time estimation when cloud processes the task requested from a mobile device. We found that the response time is effected by new sensing data generation rate and synchronization period through simulation results.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.107-111
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• 2021

A 3D point cloud map is an essential elements in various fields, including precise autonomous navigation system. However, generating a 3D point cloud map using a single sensor has limitations due to the price of expensive sensor. In order to solve this problem, we propose a precise 3D mapping system using low-cost sensor fusion. Generating a point cloud map requires the process of estimating the current position and attitude, and describing the surrounding environment. In this paper, we utilized a commercial visual-inertial odometry sensor to estimate the current position and attitude states. Based on the state value, the 2D LiDAR measurement values describe the surrounding environment to create a point cloud map. To analyze the performance of the proposed algorithm, we compared the performance of the proposed algorithm and the 3D LiDAR-based SLAM (simultaneous localization and mapping) algorithm. As a result, it was confirmed that a precise 3D point cloud map can be generated with the low-cost sensor fusion system proposed in this paper.