• Convergence Security Journal
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• v.12 no.1
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• pp.35-42
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• 2012

Increase of data traffic and the advent of new real-time services require to change from the traditional TDM-based (Time Division Multiplexing) networks to the optical networks that soft and dynamic configuration. Voice and lease line services are main service area of the traditional TDM-based networks. This optical network became main infrastructure that offer many channel that can convey data, video, and voice. To provide high resilience against failures, Packet-optical networks must have an ability to maintain an acceptable level of service during network failures. Fast and resource optimized lightpath restoration strategies are urgent requirements for the near future Packet-optical networks with a Generalized Multi-Protocol Label Switching(GMPLS) control plane. The goal of this paper is to provide packet-optical network with a hierarchical multi-layer recovery in order to fast and coordinated restoration in packet-optical network/GMPLS, focusing on new implementation information. The proposed schemes do not need an extension of optical network signaling (routing) protocols for support.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.141-146
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• 2019

These days, there are many applications using neural networks as parts of their system. On the other hand, adversarial examples have become an important issue concerining the security of neural networks. A classifier in neural networks can be fooled and make it miss-classified by adversarial examples. There are many research to encounter adversarial examples by using denoising methods. Some of them using GAN (Generative Adversarial Network) in order to remove adversarial noise from input images. By producing an image from generator network that is close enough to the original clean image, the adversarial examples effects can be reduced. However, there is a chance when adversarial noise can survive the approximation process because it is not like a normal noise. In this chance, we propose a research that utilizes high-level representation in the classifier by combining GAN network with a trained U-Net network. This approach focuses on minimizing the loss function on high representation terms, in order to minimize the difference between the high representation level of the clean data and the approximated output of the noisy data in the training dataset. Furthermore, the generated output is checked whether it shows minimum error compared to true label or not. U-Net network is trained with true label to make sure the generated output gives minimum error in the end. At last, the remaining adversarial noise that still exist after low-level approximation can be removed with the U-Net, because of the minimization on high representation terms.

• Nutrition Research and Practice
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• v.14 no.2
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• pp.134-142
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• 2020

BACKGROUND/OBJECTIVES: North Korean refugees (NKRs) in South Korea are a unique population as they must adapt in a new country with similar cultural traits but different social, political, and economic systems, but little research has been conducted on diet and nutrition in this population. This study examined food security, dietary behaviors, and nutrient intakes among adult NKRs living in South Korea and compared them to those of South Koreans. SUBJECTS/METHODS: The subjects were 139 adult NKRs (25 men, 114 women) living in the Seoul metropolitan area, and 417 age- and sex- matched South Korean controls (SKCs; 75 men, 342 women) selected from the Korea National Health and Nutrition Examination Survey (KNHANES). Food security and dietary behaviors (meal skipping, eating-out, meals with family, nutrition education and counseling, and nutrition label knowledge and utilization) were obtained using self-administered questionnaires. Nutrient intakes were assessed by 24-hr recall. The statistical analysis was performed using IBM SPSS ver. 23.0. RESULTS: In South Korea, food security had improved over the previous 12 months, but remained significantly poorer for NKR women than SKC women. Meal skipping was three times more frequent than for SKCs and eating-out was rare. Average energy intake was 1,509 kcal for NKR men and 1,344 kcal for NKR women, which was lower than those of SKCs (2,412 kcal and 1,789 kcal, respectively). Significantly more NKRs (men 24.0%, women 21.9%) showed simultaneously deficient intake in energy, calcium, iron, vitamin A, and riboflavin than SKCs (men 2.7% (P = 0.003), women 7.0% (P < 0.001)). NKR women had a significantly higher index of nutrient quality (INQ) for some nutrients than SK women. CONCLUSIONS: This study reports significant differences in food security, dietary behaviors, and nutrient intakes between NKRs and SKCs. Generally, NKRs reported lower intakes despite improved food security, but relatively good INQs across nutrients. Further research is needed to understand processes of food choice and consumption among NKRs to provide appropriate support aimed at improving diets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2223-2242
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• 2019

Distance Vector-Hop (DV-Hop) algorithm is widely used in node localization. It often suffers the wormhole attack. The current researches focus on Double-Wormhole-Node-Link (DWNL) and have limited attention to Multi-Wormhole-Node-Link (MWNL). In this paper, we propose a security DV-Hop algorithm (AMLDV-Hop) to resist MWNL. Firstly, the algorithm establishes the Neighbor List (NL) in initialization phase. It uses the NL to find the suspect beacon nodes and then find the actually attacked beacon nodes by calculating the distances to other beacon nodes. The attacked beacon nodes generate and broadcast the conflict sets to distinguish the different wormhole areas. The unknown nodes take the marked beacon nodes as references and mark themselves with different numbers in the first-round marking. If the unknown nodes fail to mark themselves, they will take the marked unknown nodes as references to mark themselves in the second-round marking. The unknown nodes that still fail to be marked are semi-isolated. The results indicate that the localization error of proposed AMLDV-Hop algorithm has 112.3%, 10.2%, 41.7%, 6.9% reduction compared to the attacked DV-Hop algorithm, the Label-based DV-Hop (LBDV-Hop), the Secure Neighbor Discovery Based DV-Hop (NDDV-Hop), and the Against Wormhole DV-Hop (AWDV-Hop) algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.5
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• pp.1087-1097
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• 2017

As the penetration rate of smartphones increases, the number of malicious codes targeting smartphones is increasing. I 360 Security 's smartphone malware statistics show that malicious code increased 437 percent in the first quarter of 2016 compared to the fourth quarter of 2015. In particular, malicious applications, which are the main means of distributing malicious code on smartphones, are aimed at leakage of user information, data destruction, and money withdrawal. Often, it is operated by an API, which is an interface that allows you to control the functions provided by the operating system or programming language. In this paper, we propose a mechanism to detect malicious application based on the similarity of API pattern in normal application and malicious application by learning pattern of API in application derived from static analysis. In addition, we show a technique for improving the detection rate and detection rate for each label derived by using the corresponding mechanism for the sample data. In particular, in the case of the proposed mechanism, it is possible to detect when the API pattern of the new malicious application is similar to the previously learned patterns at a certain level. Future researches of various features of the application and applying them to this mechanism are expected to be able to detect new malicious applications of anti-malware system.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.233-240
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• 2019

In order to predict and respond to cyber attacks, a number of security companies quickly identify the methods, types and characteristics of attack techniques and are publishing Security Intelligence Reports(SIRs) on them. However, the SIRs distributed by each company are huge and unstructured. In this paper, we propose a framework that uses five analytic techniques to formulate a report and extract key information in order to reduce the time required to extract information on large unstructured SIRs efficiently. Since the SIRs data do not have the correct answer label, we propose four analysis techniques, Keyword Extraction, Topic Modeling, Summarization, and Document Similarity, through Unsupervised Learning. Finally, has built the data to extract threat information from SIRs, analysis applies to the Named Entity Recognition (NER) technology to recognize the words belonging to the IP, Domain/URL, Hash, Malware and determine if the word belongs to which type We propose a framework that applies a total of five analysis techniques, including technology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4968-4986
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• 2017

In this paper, we primarily address the difficulty of automatic generation of a plausible depth map from a single image in an unstructured environment. The aim is to extrapolate a depth map with a more correct, rich, and distinct depth order, which is both quantitatively accurate as well as visually pleasing. Our technique, which is fundamentally based on a preexisting DepthTransfer algorithm, transfers depth information at the level of superpixels. This occurs within a framework that replaces a pixel basis with one of instance-based learning. A vital superpixels feature enhancing matching precision is posterior incorporation of predictive semantic labels into the depth extraction procedure. Finally, a modified Cross Bilateral Filter is leveraged to augment the final depth field. For training and evaluation, experiments were conducted using the Make3D Range Image Dataset and vividly demonstrate that this depth estimation method outperforms state-of-the-art methods for the correlation coefficient metric, mean log10 error and root mean squared error, and achieves comparable performance for the average relative error metric in both efficacy and computational efficiency. This approach can be utilized to automatically convert 2D images into stereo for 3D visualization, producing anaglyph images that are visually superior in realism and simultaneously more immersive.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2354-2365
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• 1997

This paper presents a design of an access control mechanism that can resolves the complicated problems of access control requirements in modern information communication applications. In this paper, we proposed an integrated information model which can satisfy the combined goals of confidentiality, integrity and availability of any resource. We defined an integrated information model from the view points of identity-based, rule-based and role-based policy and implemented six access control operations. The proposed integrated information model can protect to unauthorized access to any resource based on the multilevel security policies of security label, integrity level, role and ownership.

• Convergence Security Journal
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• v.13 no.6
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• pp.3-9
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• 2013

With the rapidly changing information communication environment and development of technologies, the informati on networks are evolved from traditional fixed form to an active variable network that flexible large variety of data can be transferred. To reflect the needs of users, the next generation using DWDM (Dense Wavelength Division M ultiplexing) transmission system and OXC (Optical Cross Connect) form a dynamic network. After that GMPLS (Ge neralized Multi-Protocol Label Switching) can be introduced to dynamically manage and control the Reconfigurable Optical Add-drop Multiplexer (ROADM)/Photonic Cross Connect (PXC) based network. This paper propose a softw are architecture of Path Computation Element (PCE) protocol that has proposed by Internet Engineering Task Force (IETF) to path computation. The functional blocks and Application Programming Interface (API) of the PCE protoco l implementation are also presented.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.