Vol. 46, No. 2,
Feb. 2019
Digital Library
Distribution of Malicious Apps Considering App Categories and Development Tools in Major Android Markets
Jihwan Oh, Myeonggeon Lee, SeongJe Cho, Sangchul Han
http://doi.org/10.5626/JOK.2019.46.2.109
According to recent cyber security analysis reports, there are numerous malicious apps available in online markets. In this paper, we analyzed the portion of malicious apps by market, main category, and cross-platform development tools for apps distributed on Android"s official market (Google Play) and a third-party market (Amazon Appstore). The apps were collected from the 13 main categories of the markets and examined using the VirusTotal service. We classified them into benign app, malware and potentially-unwanted applications (PUA). The percentage of each category and development tool used was then quantified. The distribution of malicious apps created with primary cross-platform development tools was also measured. Out of the total 22,615 apps collected, 4,741 of them were found to be malicious apps. The percentage of malicious apps was found to be 14.39% and 24.85% in Google play and Amazon Appstore respectively. The categories with the highest percentage of malicious apps were Utilities (19.8%) and Weather (19.1%) in Google Play, and Social (40.2%), Travel&Local (36.3%) and Weather (34.9%) in Amazon Appstore. Caution should be exercised when users install apps from these categories. Additionally, the percentage of malicious apps written using cross-platform development tools was 17.8%, a dramatic increase in comparison to previous statistics.
Computation of Near-Zero Hausdorff Distance Between Triangle Mesh and Quad Mesh
Yunku Kang, Seung-Hyun Yoon, Min-Ho Kyung, Myung-Soo Kim
http://doi.org/10.5626/JOK.2019.46.2.116
The Hausdorff distance between two objects can be used as a measure for their similarity, but its precise computation is a significantly challenging problem that is as complex as the objects are similar, since it is more difficult to locate where the Hausdorff distance occurs. In this paper, we solve this obstacle and present an algorithm that precisely computes the Hausdorff distance between a triangle mesh model and a quad mesh model. We narrow down where the Hausdorff distance occurs by iteratively partitioning the models into smaller pieces and removing irrelevant ones. The “match” that we set for each piece and its corresponding upper bound for the Hausdorff distance enable us to decide whether to keep the piece. The Hausdorff distance thus computed can be used to evaluate quad meshes that approximate triangle meshes.
GAN considering ERF for High-resolution Map Generation
http://doi.org/10.5626/JOK.2019.46.2.122
The paper proposes a network structure for a generative adversarial network (GAN) suitable for high resolution image transformation. For analysis of the resolution classification relation necessary for high resolution image conversion, the effective size of the receptive fields of each encoder is calculated and new connection imbalance fields defined. We can reduce the total number of layers by connecting the encoder and decoder to the patch size, we reduce the total number of layers and the appropriate effective receptive fields and parameter usability confirmed through experiments. To solve the problem of simultaneously providing resolution and classification in high resolution image conversion, a network structure capable of converting high resolution satellite images is suggested experimentally. Additionally, the validity of the network structure that simultaneously improves the resolution and classification is confirmed by comparing and analyzing the receptive fields of the proposed network and the existing network’s receptive fields. The proposed network is then quantitatively verified by comparing the proposed network with the existing network by use of objective numerical value through SSIM, an image similarity analysis method.
Incorrect Triple Detection Using Knowledge Base Embedding and Relation Model
Ji-Hun Hong, Hyun-Young Choi, Wan-Gon Lee, Young-Tack Park
http://doi.org/10.5626/JOK.2019.46.2.131
Recently, with the increase of the amount of information due to the development of the Internet, there has been an increased interest in research using a large-capacity knowledge base. Additionally, studies are being conducted to complete the knowledge base as it uses become widely used in various studies. However, there has been lack of research to detect error triples in the knowledge base. This paper, we proposes the embedding of an algorithm to detect the error triple in the knowledge base, the utilization of the clustered embedding model and the four relational models, which are typical algorithms of triple classification. Additionally, a relation ensemble model was generated using the results of the single embedding models and the embedding ensemble model similarly generated using the results of the single embedding models. The error triple detection results were then compared and measured through the model verification indexes.
A UAV Situational Awareness Method through the Threat-Related Relation Reasoning between UAV and Surrounding Objects
Seok-Hyun Bae, Myung-Joong Jeon, Hyun-Kyu Park, Young-Tack Park, Hyung-Sik Yoon, Yun-Geun Kim
http://doi.org/10.5626/JOK.2019.46.2.141
As the technological capabilities of UAV(Unmanned Aerial Vehicles) improves, studies are being carried out to intelligently analyze and understand the situation of UAV in order to gain access to the target area while recognizing and avoiding various risks. To achieve the mission of UAV, it is necessary to judge the situation accurately and quickly. To do this, this paper proposes ways to infer the threat-related relationship between an UAV and perceived surrounding objects through a 3 step approach and provide abstract information about the situation of UAV. The first step is to instantiate the object data recognized by UAV to be utilized for ontology and rule-based reasoning. The second step is to define the priority of instantiated object data and to infer the threat-related relationship between them. Finally, recognizing the situation through the relationship inference that takes into account the association between current and past inferred relationships. To evaluation the performance of the proposed method, a virtual UAV environment simulator was built and tested the data 1,000 times that were randomly generated through five sequential UAV moving point paths. Eight kinds of objects could be recognized in UAV path and ten kinds of relationships can be inferred. Overall performance of situation Awareness was an average of 91 percent.
Privacy Protection Method based on Multi-Object Authentication in Intelligent CCTV Environment
http://doi.org/10.5626/JOK.2019.46.2.154
In the intelligent CCTV surveillance environment, personal identity is confirmed based on face recognition. However, the recognition rate of the current face recognition technology is still faulty. In particular, face recognition may not work correctly due to various causes such as CCTV shot quality, weather, personal pose and facial expression, hairstyle, lighting condition, and so on. In this case, there is a great risk of exposing object`s privacy information in the video surveillance environment due to erroneous object judgment. The proposed method can increase the recognition rate of objects based on the CCTV-RFID hybrid authentication method, and thus protect the privacy of the image object.
A Technique for Updating Method Calls by Utilizing Software Change Rules
http://doi.org/10.5626/JOK.2019.46.2.161
Previous studies have proposed a method of extracting change rules by analyzing differences in releases of the client software framework with an aim of reducing developer’s efforts in updating the client software. However, these studies only discuss the generation of change rules and do not directly discuss updating the client system. To overcome this limitation, we propose a method that automatically updates the method calls in the client by using the change rules produced by the existing rule generation tools. We also implement the proposed method as a tool and evaluate how well the method automatically updates method calls using the extracted change rules. Results showed that 279 out of 547 method calls were automatically updated and only 2 compilation errors were found. This study contributes to research into reducing the efforts of client developers to update method calls after any method changes to the framework.
A Study on P2P Lending Deadline Prediction Model based on Machine Learning
http://doi.org/10.5626/JOK.2019.46.2.174
Recently, there has been an increase in P2P lending users, a product that supports investments through lending among individuals using online platforms. However, since P2P lending`s investors have to take financial risks, the investors may fail to investment due to the close of investment while they considering whether to invest or not. This paper predicts how long an investment product will take from a certain point to the close in order to provide deadline information for P2P loan investment products. To predicts the investment deadline, we have transforms into Timeseries data and Step data based on investment information on actual P2P products. The regression, classification, and time series prediction model were generated using machine learning algorithm. The results of the performance evaluation showed that in the Timeseries data-based model, the Multi-layer Perceptron regression model and the classification model showed the highest performance at 0.725 and 0.703 respectively. The Step data-based model was also the highest with the Multi-layer Perceptron regression model and the classification model at 0.782 and 0.651 respectively.
Evaluation of Interest Point Detectors for Data Authentication in Wireless Multimedia Sensor Network (WMSN)
http://doi.org/10.5626/JOK.2019.46.2.184
In Wireless Multimedia Sensor Networks (WMSNs), authentication of multimedia data is very important because the data can be used in making crucial decisions. This study evaluates interest point detectors in terms of resilience to channel error occurred in WMSNs, robustness to JPEG compression, and sensitivity to image tampering. SIFT, SURF, ORB, AKAZE, SADDLE and HOG were evaluated with USC-SIPI image database by computing recall and precision between the original images and modified images by channel errors and JPEG compression and tampering. In addition, median filter and Gaussian filter were applied to reduce channel error and quantization errors from JPEG compression respectively and produced significant performance. AKAZE showed best performance for all conditions of experiments. The evaluation of interest point detectors showed the possibility of their application to authentication in WMSNs.
Efficient Similarity Search of Multi-Attribute Records using An Optimal Attribute Assignment
http://doi.org/10.5626/JOK.2019.46.2.193
In this paper, we investigate the problem of record similarity search in cases where the given records consist of multiple attribute data. Despite the fact that similarity measures exist, they only quantify the similarity between two data sets leaving out the similarities between attributes in the record sets. To address this problem, we propose a record similarity measure which considers similarities among attributes in records. We also develop a novel filtering technique to efficiently generate candidate records with respect to a record similarity threshold. In addition, we propose an efficient verification technique that verifies if a candidate is a true match. Through an experimental study, we show that the proposed techniques can be used to search similar records with high efficiency and precision.
A CNN-based Column Prediction Model for Generating SQL Queries using Natural Language
Yoonki Jeong, Dongmin Kim, Jongwuk Lee
http://doi.org/10.5626/JOK.2019.46.2.202
To retrieve massive data using relational database management system (RDBMS), it is important to understanding of table schemas and SQL grammar. To address this issue, many studies have recently been carried out to generate an SQL query from a natural language question. However, the existing works suffer mostly from predicting columns at where clause and the accuracy is greatly reduced when there are multiple columns to be predicted. In this paper, we propose a convolutional neural network model with column attention mechanism that effectively extracts the latent representation of input question which helps column prediction of the model. The experiment shows that our model outperforms the accuracy of the existing model (SQLNet) by 6%.
Practically Secure Key Exchange Scheme based on Neural Network
Sooyong Jeong, Dowon Hong, Changho Seo
http://doi.org/10.5626/JOK.2019.46.2.208
Key exchange is one of the major aspects in cryptography. Recently, compared to the existing key exchange schemes, more efficient key exchange schemes have been proposed based on neural network learning. After the first key exchange scheme based on neural network was proposed, various attack models have been suggested in security analysis. Hebbian learning rule is vulnerable to majority attack which is the most powerful attack. Anti Hebbian learning rule is secure against majority attack has a limitation in efficiency, so we can only use key exchange scheme based on random walk learning rule which is more secure and efficient than the others. However, if we use random walk learning rule, the efficiency which is advantage about neural cryptography is reduced than the other learning rules. In this paper we analyze random walk and neural cryptography, and we propose new learning rule which is more efficient than existing random walk learning rule. Also, we theoretically analyze about key exchange scheme which is uses new learning rule and verify the efficiency and security by implementing majority attack model.
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