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

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.4
• /
• pp.115-124
• /
• 2011

There has been significant research in security technology such as e-passport standards, as e-passports have been introduced internationally. E-passports combine the latest security technologies such as smart card, public key infrastructure, and biometric recognition, so that these technologies can prevent unauthorized copies and counterfeits. Biometric information stored in e-passports is the most sensitive personal information, and it is expected to bring the highest risk of damages in case of its forgery or duplication. The present e-passport standards cannot handle security features that verify whether its biometric information is copied or not. In this paper, we propose an e-passport security technology in which biometric watermarking is used to prevent the copy of biometric information in the e-passport. The proposed method, biometric watermarking, embeds the invisible date of acquisition into the original data during the e-passport issuing process so that the human visual system cannot perceive its invisibly watermarked information. Then the biometric sample, having its unauthorized copy, is retrieved at the moment of reading the e-passport from the issuing database. The previous e-passport security technology placed an emphasis on both access control readers and anti-cloning chip features, and it is expected that the proposed feature, copy protection of biometric information, will be demanded as the cases of biometric recognition to verify personal identity information has increased.

• Journal of Internet of Things and Convergence
• /
• v.7 no.2
• /
• pp.7-12
• /
• 2021

Wisdom Contents are created with experiences and ideas of multiple authors, and consumed in Internet based Social Network Services that are not subjected to regional restrictions. Existing copyright management systems are designed for the protection of professional authors' rights, and effective in domestic area. On the contrary, the blockchain protocol is subjected to the service and the block is added by the consensus of participating nodes. If the data is stored to the blockchain, it cannot be modified or deleted. In this paper, we propose the blockchain based undeniable multi-signature scheme for the protection of multiple authorship on Wizdom Contents. The proposed scheme is consisted of co-authors' common public key generation, multi-signature generation and verification protocols. In the undeniable signature scheme, the signature cannot be verified without help of the signer. The proposed scheme is best suited to the contents purchase protocol. All co-authors cannot deny the fairness of the automated profit distribution through the verification of multiple authorship on Wizdom Contents.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.6
• /
• pp.1247-1254
• /
• 2020

With the advent of the smart home era, equipment that provides confidentiality or performs authentication exists in various places in real life. Accordingly security against physical attacks is required for encryption equipment and authentication equipment. In particular, fault injection attack that artificially inject a fault from the outside to recover a secret key or bypass an authentication process is one of the very threatening attack methods. Fault sources used in fault injection attacks include lasers, electromagnetic, voltage glitches, and clock glitches. Fault injection attacks are classified into single fault injection attacks and multiple fault injection attacks according to the number of faults injected. Existing multiple fault injection systems generally use a single fault source. The system configured to inject a single source of fault multiple times has disadvantages that there is a physical delay time and additional equipment is required. In this paper, we propose a multiple fault injection system using heterogeneous fault sources. In addition, to show the effectiveness of the proposed system, the results of a multiple fault injection attack against Riscure's Piñata board are shown.

• Journal of Information Technology Services
• /
• v.21 no.2
• /
• pp.97-107
• /
• 2022

Recently, interest in smart factories is increasing. Investments to improve intelligence/automation are also being made continuously in manufacturing plants. Facility automation based on sensor data collection is now essential. In addition, we are operating our factories based on data generated in all areas of production, including production management, facility operation, and quality management, and an integrated standard information system. When producing LCD polarizer products, it is most important to link trace information between data generated by individual production processes. All systems involved in production must ensure that there is no data loss and data integrity is ensured. The large-capacity data collected from individual systems is composed of key values linked to each other. A real-time quality analysis processing system based on connected integrated system data is required. In this study, large-capacity data collection, storage, integration and loss prevention methods were presented for optimization of LCD polarizer production. The identification Risk model of inspection products can be added, and the applicable product model is designed to be continuously expanded. A quality inspection and analysis system that maximizes the yield rate was designed by using the final inspection image of the product using big data technology. In the case of products that are predefined as analysable products, it is designed to be verified with the big data knn analysis model, and individual analysis results are continuously applied to the actual production site to operate in a virtuous cycle structure. Production Optimization was performed by applying it to the currently produced LCD polarizer production line.

• The Journal of the Korea Contents Association
• /
• v.22 no.2
• /
• pp.617-629
• /
• 2022

This study empirically analyzed the mediating effect of technology absorption capacity in the relationship between technological innovation of ship parts manufacturing companies on business performance. Through this, it will be possible to provide implications for improvement plans for management strategy establishment related to technology development in the future. In order to achieve the purpose of this study, R&D, marketing, production·A total of 362 people working in manufacturing and finance/accounting departments were selected as subjects for this study. As a result of this study, it was found that technological innovation and technology absorption capacity of ship parts manufacturing companies have a positive (+) effect on business performance. The conclusion based on these research results is that the potential absorption capacity and realized absorption capacity constituting technology absorption capacity are judged to be the main key factors between technological innovation and management performance, such as continuous technological capacity accumulation. From a practical point of view, the ship parts manufacturing industry needs to focus on its ability to absorb smart parts process technology.

• Genomics & Informatics
• /
• v.20 no.2
• /
• pp.19.1-19.15
• /
• 2022

Alfalfa (Medicago sativa) is an important food and feed crop which rich in mineral sources. The WUSCHEL-related homeobox (WOX) gene family plays important roles in plant development and identification of putative gene families, their structure, and potential functions is a primary step for not only understanding the genetic mechanisms behind various biological process but also for genetic improvement. A variety of computational tools, including MAFFT, HMMER, hidden Markov models, Pfam, SMART, MEGA, ProtTest, BLASTn, and BRAD, among others, were used. We identified 34 MsWOX genes based on a systematic analysis of the alfalfa plant genome spread in eight chromosomes. This is an expansion of the gene family which we attribute to observed chromosomal duplications. Sequence alignment analysis revealed 61 conserved proteins containing a homeodomain. Phylogenetic study sung reveal five evolutionary clades with 15 motif distributions. Gene structure analysis reveals various exon, intron, and untranslated structures which are consistent in genes from similar clades. Functional analysis prediction of promoter regions reveals various transcription binding sites containing key growth, development, and stress-responsive transcription factor families such as MYB, ERF, AP2, and NAC which are spread across the genes. Most of the genes are predicted to be in the nucleus. Also, there are duplication events in some genes which explain the expansion of the family. The present research provides a clue on the potential roles of MsWOX family genes that will be useful for further understanding their functional roles in alfalfa plants.

• Nuclear Engineering and Technology
• /
• v.53 no.7
• /
• pp.2387-2395
• /
• 2021

Seismic qualifications of electrical equipment, such as cabinet systems, have been emerging as the key area of nuclear power plants in Korea since the 2016 Gyeongju earthquake, including the high-frequency domain. In addition, electrical equipment was sensitive to the high-frequency ground motions during the past earthquake. Therefore, this paper presents the rocking behavior of the electrical cabinet system subjected to Reg. 1.60 and UHS. The high fidelity finite element (FE) model of the cabinet related to the shaking table test data was developed. In particular, the first two global modes of the cabinet from the experimental test were 16 Hz and 24 Hz, respectively. In addition, 30.05 Hz and 37.5 Hz were determined to be the first two local modes in the cabinet. The high fidelity FE model of the cabinet using the ABAQUS platform was extremely reconciled with shaking table tests. As a result, the dynamic properties of the cabinet were sensitive to electrical instruments, such as relays and switchboards, during the shaking table test. In addition, the amplification with respect to the vibration transfer function of the cabinet was observed on the third floor in the cabinet due to localized impact corresponding to the rocking phenomenon of the cabinet under Reg.1.60 and UHS. Overall, the rocking of the cabinet system can be caused by the low-frequency oscillations and higher peak horizontal acceleration.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.5
• /
• pp.793-801
• /
• 2022

Most Bluetooth devices are commonly used in various ways these days, but they can be often lost due to small-size devices. However, most Bluetooth protocol do not provide authentication functions to legitimate owners, and thus someone who obtains the lost Bluetooth device can easily connect to their smart devices to use it. In this paper, we propose NBAS can authenticates legitimate owners using NFT on lossy Bluetooth devices.NBAS generates a digital wallet on the blockchain using the decentralized network Ethereum blockchain and facilitating the MAC address of the Bluetooth device in the digital wallet. The owner of the wallet uses a private key to certify the Bluetooth device using NFT. The initial pairing time of NBAS was 10.25 sec, but the reconnection time was 0.007 sec similar to the conventional method, and the pairing rejection time for unapproved users was 1.58 sec on average. Therefore, the proposed NBAS effectively shows the device authentication over the conventional Bluetooth.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.195-206
• /
• 2022

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is a significant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320×320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.