• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.599-612
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• 2019

Cloud networks are used to provide various services. As services are increasingly deployed using cloud networks, there are a number of resources in the cloud that leverage a variety of environments and protocols. However, there is a security intrusion on these resources, and research on cloud network vulnerability detection is required as threats to cloud resources emerge. In this paper, we propose a vulnerability detection scheme using STRIDE and HARM for vulnerability detection of resources utilizing various environments and protocols, and present cloud network vulnerability detection scheme through vulnerability detection scenario composition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3384-3400
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• 2021

With the advent of the data era, people pay much more attention to data corruption. Aiming at the problem that the majority of existing schemes do not support corruption detection of ciphertext data stored in cloud environment, this paper proposes a data corruption detection scheme based on ciphertexts in cloud environment (DCDC). The scheme is based on the anomaly detection method of Gaussian model. Combined with related statistics knowledge and cryptography knowledge, the encrypted detection index for data corruption and corruption detection threshold for each type of data are constructed in the scheme according to the data labels; moreover, the detection token for data corruption is generated for the data to be detected according to the data labels, and the corruption detection of ciphertext data in cloud storage is realized through corresponding tokens. Security analysis shows that the algorithms in the scheme are semantically secure. Efficiency analysis and simulation results reveal that the scheme shows low computational cost and good application prospect.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.34-37
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• 2005

An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection. Unlike many existing cloud detection schemes which use thresholding and statistical methods, we used the artificial neural network methods, the multi-layer perceptrons (MLP) with back-propagation algorithm and radial basis function (RBF) networks for cloud detection from Geostationary satellite images. We have used a simple scene (a mixed scene containing only cloud and clear sky). The main results show that the neural networks are able to handle complex atmospheric and meteorological phenomena. The experimental results show that two methods performed well, obtaining a classification accuracy reaching over 90 percent. Moreover, the RBF model is the most effective method for the cloud classification.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.239-251
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• 2018

In existing cloud services, information security and privacy concerns have been worried, and have become one of the major factors that hinder the popularization and promotion of cloud computing. As the cloud computing infrastructure, the security of virtual machine systems is very important. This paper presents an immune-inspired intrusion detection model in virtual machines of cloud computing environment, denoted I-VMIDS, to ensure the safety of user-level applications in client virtual machines. The model extracts system call sequences of programs, abstracts them into antigens, fuses environmental information of client virtual machines into danger signals, and implements intrusion detection by immune mechanisms. The model is capable of detecting attacks on processes which are statically tampered, and is able to detect attacks on processes which are dynamically running. Therefore, the model supports high real time. During the detection process, the model introduces information monitoring mechanism to supervise intrusion detection program, which ensures the authenticity of the test data. Experimental results show that the model does not bring much spending to the virtual machine system, and achieves good detection performance. It is feasible to apply I-VMIDS to the cloud computing platform.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.461-463
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• 2003

The information on the cloud presence within a instantaneous field of view is the first step toward the derivation of many other geophysical parameters. Here, we first applied the current MODIS cloud detection algorithm developed by University of Wisconsin and compared the results to a visual interpretation of composite data, especially during the daytime. Most of cases, the detection algorithm performs very well, except a few cases with over-detection. One of the reasons for the false detection is due to the time independent use of land information which affects the threshold values of visible channel test. In the presentation, we show detailed analysis of the current cloud detection algorithm and suggest possible way to overcome the current shortfall.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.107-113
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• 2012

Cloud computing has brought changes to the IT environment. Due to the spread of LTE, users of cloud services are growing more. This which provides IT resources to meet the needs of users of cloud services are noted as a core industry. But it is not activated because of the security of personal data and the safety of the service. In order to solve this, intrusion detection system is constructed as follows. This protects individual data safely which exists in the cloud and also protects information exhaustively from malicious attack. The cause of most attack risk which exists to cloud computing can find in distributed environment. In this study, we analyzed about necessary property of network-based intrusion detection system that process and analyze large amount of data which occur in cloud computing environment. Also, we studied functions which detect and correspond attack occurred in interior of virtualization.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.3
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• pp.302-309
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• 2020

Early-detection and monitoring of toxic chemical gas cloud with chemical detector is essential for reducing the number of casualties. Conventional method for chemical detection and reconnaissance has the limitation in approaching to chemically contaminated site and prompt understanding for the situation. Stand-off detector can detect and identify the chemical gas at a long distance but it cannot know exact distance and position. Chemical detection UAV is an emerging platform for its high mobility and operation safety. In this study, we have conducted chemical gas cloud detection with the stand-off chemical detector and the chemical detection UAV. DMMP vapor was generated in the area where the cloud can be detected through the field of view(FOV) of stand-off chemical detector. Monitoring the vapor cloud with standoff detector, the chemical detection UAV moved back and forth at the area DMMP vapor being generated to detect the chemical contamination. The hybrid detection system with standoff cloud detection and point detection by chemical sensors with UAV seems to be very efficient as a new concept of chemical detection.

• Journal of Multimedia Information System
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• v.7 no.2
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• pp.125-136
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• 2020

Nowadays, cloud computing is becoming more popular among companies. However, the characteristics of cloud computing such as a virtualized environment, constantly changing, possible to modify easily and multi-tenancy with a distributed nature, it is difficult to perform attack detection with traditional tools. This work proposes a solution which aims to collect traffic packets data by using Flume and filter them with Spark Streaming so it is possible to only consider suspicious data related to HTTP Slow Rate Denial-of-Service attacks and reduce the data that will be stored in Hadoop Distributed File System for analysis with the FP-Growth algorithm. With the proposed system, we also aim to address the difficulties in attack detection in cloud environment, facilitating the data collection, reducing detection time and enabling an almost real-time attack detection.

• Proceedings of the KSRS Conference
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• v.1
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• pp.200-203
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• 2006

Cloud detection algorithm is being developed as major one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-1R and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithm and preliminary test result of both algorithms.