• 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.

• Economic and Environmental Geology
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• v.38 no.1
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• pp.45-55
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• 2005

Traditional GIS-based probabilistic spatial data integration models for landslide susceptibility analysis have failed to provide the theoretical backgrounds and effective methods for integration of different types of spatial data such as categorical and continuous data. This paper applies two spatial data integration models including non-parametric empirical estimation and parametric predictive discriminant analysis models that can directly use the original continuous data within a likelihood ratio framework. Similarity rates and a prediction rate curve are computed to quantitatively compare those two models. To illustrate the proposed models, two case studies from the Jangheung and Boeun areas were carried out and analyzed. As a result of the Jangheung case study, two models showed similar prediction capabilities. On the other hand, in the Boeun area, the parametric predictive discriminant analysis model showed the better prediction capability than that from the non-parametric empirical estimation model. In conclusion, the proposed models could effectively integrate the continuous data for landslide susceptibility analysis and more case studies should be carried out to support the results from the case studies, since each model has a distinctive feature in continuous data representation.

• Journal of Microbiology and Biotechnology
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• v.33 no.7
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• pp.909-914
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• 2023

While searching for the bacteria which are responsible for degradation of pesticide in soybean field soil, a novel bacterial strain, designated 5-5^T, was isolated. The cells of the strain were Gram-staining-positive, aerobic and non-motile rods. Growth occurred at 10-42℃ (optimum, 30℃), pH 5.5-9.0 (optimum, pH 7.0-7.5), and 0-2% (w/v) NaCl (optimum, 1%). The predominant fatty acids were C_15:0 anteiso, C_17:0 anteiso, and summed feature 8 (C_18:1 ω7c and/or C_18:1 ω6c). The predominant menaquinone was MK-9 (H₂). Diphosphatidylglycerol, glycolipids, phosphatidylinositol, and phosphatidylglycerol were the major polar lipids. Phylogenetic analysis of 16S rRNA gene sequences indicated that strain 5-5^T is a member of the genus Sinomonas and its closest relative is Sinomonas humi MUSC 117^T, sharing a genetic similarity of 98.4%. The draft genome of strain 5-5^T was 4,727,205 bp long with an N50 contig of 4,464,284 bp. Genomic DNA G+C content of strain 5-5^T was68.0 mol%. The average nucleotide identity (ANI) values between strain 5-5^T and its closest strains S. humi MUSC 117^T and S. susongensis A31^T were 87.0, and 84.3 % respectively. In silico DNA-DNA hybridization values between strain 5-5^T and its closest strains S. humi MUSC 117^T and S. susongensis A31^T were 32.5% and 27.9% respectively. Based on the ANI and in silico DNA-DNA hybridization analyses, the 5-5^T strain was considered as novel species belonging to the genus Sinomonas. On the basis of the results from phenotypic, genotypic and chemotaxonomic analyses, strain 5-5^T represents a novel speciesof the genus Sinomonas, for which the name Sinomonas terrae sp. nov. is proposed. The type strain is 5-5^T (=KCTC 49650^T =NBRC 115790^T).

• Journal of Microbiology and Biotechnology
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• v.33 no.10
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• pp.1292-1298
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• 2023

PAMB 00755^T, a bacterial strain, was isolated from Korean fir leaves. The strain exhibits yellow colonies and consists of Gram-negative, non-motile, short rods or ovoid-shaped cells. It displays optimal growth conditions at 20℃, 0% NaCl, and pH 6.0. Results of 16S rRNA gene-based phylogenetic analyses showed that strain PAMB 00755^T was most closely related to Sphingomonas chungangi MAH-6^T (97.7%) and Sphingomonas polyaromaticivorans B2-7^T (97.4%), and ≤96.5% sequence similarity to other members of the genus Sphingomonas. The values of average nucleotide identity (79.9-81.3%), average amino acid identity (73.3-75.9%), and digital DNA-DNA hybridization (73.3-75.9%) were significantly lower than the threshold values for species boundaries; these overall genome-related indexes (OGRI) analyses indicated that the strain represents a novel species. Genomic analysis revealed that the strain has a 4.4-Mbp genome encoding 4,083 functional genes, while the DNA G+C content of the whole genome is 66.1%. The genome of strain PAMB 00755^T showed a putative carotenoid biosynthetic cluster responsible for its antioxidant activity. The respiratory quinone was identified as ubiquinone 10 (Q-10), while the major fatty acids in the profile were identified as C_18:1ω7c and/or C_18:1ω6c (summed feature 8). The major polar lipids of strain PAMB 00755^T were diphosphatidylglycerol, phosphatidylethanolamine, sphingoglycolipid, and phosphatidylcholine. Based on a comprehensive analysis of genomic, phenotypic, and chemotaxonomic characteristics, we proposed the name Sphingomonas abietis sp. nov. for this novel species, with PAMB 00755^T as the type strain (= KCTC 92781^T = GDMCC 1.3779^T).

• Journal of Industrial Convergence
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• v.21 no.9
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• pp.41-48
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• 2023

Recently, there has been a sharp increase in the damages caused by ransomware across various sectors of society, including individuals, businesses, and nations. Ransomware is a malicious software that infiltrates user computer systems, encrypts important files, and demands a ransom in exchange for restoring access to the files. Due to its diverse and sophisticated attack techniques, ransomware is more challenging to detect than other types of malware, and its impact is significant. Therefore, there is a critical need for accurate detection and mitigation methods. To achieve precise ransomware detection, an inference engine of a detection system must possess knowledge of ransomware features. In this paper, we propose a model to extract and classify the characteristics of ransomware for accurate detection of ransomware, calculate the similarity of the extracted characteristics, reduce the dimension of the characteristics, group the reduced characteristics, and classify the characteristics of ransomware into attack tools, inflow paths, installation files, command and control, executable files, acquisition rights, circumvention techniques, collected information, leakage techniques, and state changes of the target system. The classified characteristics were applied to the existing ransomware to prove the validity of the classification, and later, if the inference engine learned using this classification technique is installed in the detection system, most of the newly emerging and variant ransomware can be detected.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.273-285
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• 2024

With the active utilization of Online Judge (OJ) systems in the field of education, various studies utilizing learner data have emerged. This research proposes a problem recommendation based on a user-based collaborative filtering approach with learner data to support learners in their problem selection. Assistance in learners' problem selection within the OJ system is crucial for enhancing the effectiveness of education as it impacts the learning path. To achieve this, this system identifies learners with similar problem-solving tendencies and utilizes their problem-solving history. The proposed technique has been implemented on an OJ site in the fields of algorithms and programming, operated by the Chungbuk Education Research and Information Institute. The technique's service utility and usability were assessed through expert reviews using the Delphi technique. Additionally, it was piloted with site users, and an analysis of the ratio of correctness revealed approximately a 16% higher submission rate for recommended problems compared to the overall submissions. A survey targeting users who used the recommended problems yielded a 78% response rate, with the majority indicating that the feature was helpful. However, low selection rates of recommended problems and low response rates within the subset of users who used recommended problems highlight the need for future research focusing on improving accessibility, enhancing user feedback collection, and diversifying learner data analysis.

• The Korean Journal of Mycology
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• v.25 no.1 s.80
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• pp.46-67
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• 1997

From 1985 to 1993, we collected 20 isolates throughout Kangwon and obtained 6 isolates from other sources. A. mellea formed rhizomorph actively, and some of A. osroyae were poor in the formation of rhizomorph and some without formation of rhizomorph. A. tabescens was active in the growth of aerial mycelium and poor in the development of rhizomorph. In A. gallica, the mycelium development among the isolates were variable greatly, and especially in isolate A8(KNU-250), the mycelial development was similar to that of A. osroyae, but A8(KNU-250) showed the feature of A. gallica to change medium into brown color. In PCR-RFLP analysis of the IGS region in rDNA, the homology between each isolate in the A. mellea and A. ostoyae showed 100% homology. A. tabescens showed $0.919{\sim}0.974$ homology, and A. gallica showed $0.619{\sim}1.000$ homology. A19 and A12 showed 100% homology as the same group, but compared with other subgroups they showed less than 10% homology as $0.051{\sim}0.108$ value. In RAPD analyses, the isolates of A. mellea showed high homology among themselves as $0.983{\sim}1.000$, and A. ostoyae also showed high similarity. The homology between isolates of A. tabescens showed $0.594{\sim}0.953$ value because A. gallica showed $0.280{\sim}0.733$ value, and the variations between isolates were greater than other species. Especially, A19 and A22 were identified as new novel group which were remoted from other groups, and the homology between these two isolates showed 0.921 value, and the genetic similarity between these groups and other 4 groups showed less than 7% as $0.012{\sim}0.069$ value. Of 5 species identified in this study, 4 species were identified as A. mellea, A. ostoyae, A. tabescens, and A. gallica that were already reported ones and 1 species was suggested as a new specie in Korea.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.17-31
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• 2006

In this paper, passport recognition and face verification methods which can automatically recognize passport codes and discriminate forgery passports to improve efficiency and systematic control of immigration management are proposed. Adjusting the slant is very important for recognition of characters and face verification since slanted passport images can bring various unwanted effects to the recognition of individual codes and faces. Therefore, after smearing the passport image, the longest extracted string of characters is selected. The angle adjustment can be conducted by using the slant of the straight and horizontal line that connects the center of thickness between left and right parts of the string. Extracting passport codes is done by Sobel operator, horizontal smearing, and 8-neighborhood contour tracking algorithm. The string of codes can be transformed into binary format by applying repeating binary method to the area of the extracted passport code strings. The string codes are restored by applying CDM mask to the binary string area and individual codes are extracted by 8-neighborhood contour tracking algerian. The proposed RBF network is applied to the middle layer of RBF network by using the fuzzy logic connection operator and proposing the enhanced fuzzy ART algorithm that dynamically controls the vigilance parameter. The face is authenticated by measuring the similarity between the feature vector of the facial image from the passport and feature vector of the facial image from the database that is constructed with PCA algorithm. After several tests using a forged passport and the passport with slanted images, the proposed method was proven to be effective in recognizing passport codes and verifying facial images.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.109-125
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• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.