Earticle

The paper analyzes a copyright owning firm’s incentive to enforce its copyright in the presence of file-sharing networks. I devise a two-period model where a copyright owner sells two different versions of a creator’s information good, and show that the firm’s overall profits are enhanced by a strategy of differential inter-temporal enforcement of the copyright protection, compared to strategies of no enforcement or full enforcement in both periods. If the firm enforces no copyright protection in the first period, the low-valuation consumers may make and consume copies that are imperfect substitutes for the original information good. If there is a significant increase in the willingness-to-pay of some low-valuation consumers after they experience the information good, the firm can extract the increased consumer surplus by enforcing a positive level of copyright protection in the second period. Social welfare, however, is maximized in the case of no enforcement.

최근 들어 많이 활용되고 있는 가상화 기술 중 컨테이너 가상화 기술인 도커는 경량화 된 서비스 운영환경을 제공함 으로 다양한 편의성을 제공하고 있다. 하지만, 도커를 통해 공유되는 이미지들은 누구나 쉽게 배포할 수 있기 때문 에, 출처를 정확하게 알 수 없는 경우가 많아 여러 가지 보안 취약점을 가지고 있다. 하지만, 이러한 보안 취약점을 확인하기 위해서는 도커 이미지들을 컨테이너화 하여 실행해 보고, 메모리에 로드가 된 후에야 분석이 가능하기 때 문에 악의적인 코드가 있을 경우 큰 문제를 야기할 수 있다. 이에 본 논문에서는 도커 허브나 기타 repository에서 받은 도커 이미지들의 보안 취약성을 실행전에 사전에 확인하기 위하여 도커의 이미지 파일 시스템을 분석하여 보 고, 분석된 결과를 바탕으로 도커 이미지를 로드하지 않은 상태에서 보안취약점을 분석해 보고자한다.

Docker is a virtualization technology that is used by the greatest number of people. The Docker provides a variety of conveniences by providing a lightened service operating environment. However, images that are shared with the docker can be easily distributed by anyone. Because of this, many sources are not known accurately and have various security vulnerabilities. However, in order to identify these vulnerabilities, container images of the sinker should be executed. Malicious code can cause major problems because it can be analyzed only after it is loaded into memory. In this paper, we analyze the image file system of the Docker to check the security weakness of the Docker images received from the Docker Hub or other repository before execution. Based on the analyzed results, we want to analyze security vulnerabilities without loading the image of the docker.

In the modern society, nail art reflects customers’ change of mood and their acts and preferences in addition to nail aesthetics. Dead skin cells on feet are caused by diverse bacteria and fungi such as dermatophytes. They spread various inflammatory diseases and bad smell. This study attempted to investigate how to handle such dead skin effectively for clean and healthy feet. For this, a questionnaire survey was performed against a total of 50 adult women in their 20-50s in Busan and Gyeongsangnam-do to examine how they deal with dead skin cells on their feet. To figure out an efficient away of handling such dead skin, pedi file and nail drill were used. Then, their satisfaction was comparatively analyzed. They said that they tend to get a pedicure to keep their feet and toenails clean and healthy, especially in summer. When asked what is important in selecting a salon, an employee’s skill and kindness were most responded. When asked how much they know about the pedicure, ‘toenail shaping’ and ‘toenail cutting’ were most responded. In terms of the treatment length and method, they said that a pedicure with nail drill is more effective than a pedicure with pedi files. It is anticipated that the above results would be useful for the growth and development of related industries.

现代的美甲不仅反映了人们对美的追求，还提供了一定的情绪价值、反映了问题改善等行为与消费者偏好。脚部角质由各种细菌及脚气菌等引起，可引发多种炎症性疾病及恶臭。为了解维持清洁健康的脚部角质有效管理方法，本文以釜山、庆南地区的50名20-50多岁的女性为研究对象，以问卷的形式调查了人们对脚部角质管理的认识及实态，并使用美甲锉和磨甲器进行了比较实验，分析了其满意程度。据调查，脚部护理的目的源于，在脚部裸露较多的夏季，人们想要维持脚部的健康和清洁，人们在选择美甲店时主要考虑的因素是职员的专业性和亲切度，足部管理方面，主要进行脚指甲形状与长度的调节。针对脚部管理时间和管理方法而言，磨甲器比美甲锉更加有效。本研究成果将为美甲产业的发展提供基础资料，为其应用型研究提供支持。

FUSE(Filesystem in Userspace)는 사용자 공간에서 파일 시스템을 구현할 수 있도록 지원하는 프레임워크로, 커널 수정 없이 개발이 가능하며 비특권 사용자도 마운트할 수 있어 유연성과 접근성이 높다. 이러한 특성 덕분에 다양한 파일 시스템이 FUSE 기반으로 활발히 사용되고 있다. 그러나 사용자 공간과 커널 공간 간의 반복적인 컨텍스트 스위칭과 메모리 복사 등 구조적 오버헤드는 FUSE 시스템의 성능 저하를 야기할 수 있으며, 특히 대규모 파일 처리 상황에서는 성능 병목으로 이어질 수 있다. 본 연구는 대표적인 FUSE 기반 원격 파일 시스템인 SSHFS, SMBNetFS와 비교 기준으로 비-FUSE 기반 WebDAV(Web Distributed Authoring and Versioning) 접근 방식인 Rclone WebDAV를 대상으로, 블록 크기에 따른 읽기 성능을 정량적으로 비교하였다. 실험 결과, Rclone WebDAV는 4KB에서, SSHFS는 1MB에서 가장 높은 처리량을 보였으며, 각 시스템은 블록 크기에 따라 상이한 성능 특성을 나타냈다. 본 연구는 사용자 공간 파일 시스템의 실제 성능을 정량적으로 비교함으로써, 향후 FUSE 기반 시스템의 성능 향상 방향에 대한 실용적 근거를 제공한다.

Recently, the illegal users with malicious intention utilize the file sharing site by making normal user's computer a zombie computer, which is a preliminary process for network intrusion attack. The propose scheme is divided into the method for real-time analysis for real-time tracking and the method of cooperative analysis method for non-real time analysis. By allowing the supervisors to choose the relevant analysis method selectively, we can conduct variable analysis depending on the network threat.

Recently, Microsoft unveiled a new operating system called Windows 10. As it is highly expected that Windows 10 will play a significant role in the Internet of Things era, the Portable Executable (PE) format is drawing attention even more widely than before. PE is a standard file format for executables and object code used in MS Windows operating systems. Since a number of various malwares have widely spread by exploiting vulnerabilities of PE structure, the need of automatic tools for PE-malware detection is being magnified. In this paper, we designed and developed a command-line PE file analysis tool using Python language for automatic detection of Windows malware.

Recently, the attack trends have been changed from fast and widespread malware propagation attacks to more sophisticated “targeted” attacks such as spy/adware, password stealers, ransom-ware, and botenets etc. and the attacks are tried via the automated malwares. In this situation, the malware is the most powerful weapon for the attackers. So, the attackers do not want their malwares to be reviled by anti-virus analyzer. In order to conceal their malware, malware programmers are getting utilize the anti reverse engineering techniques and code changing techniques such as the packing, encoding and encryption techniques. If the malware is packed or encrypted, then it is very difficult to analyze. Therefore, to prevent the harmful effects of malware and to generate signatures for malware detection, the packed and encrypted executable codes must initially be unpacked. The first step of unpacking is to detect the packed executable files. In this paper, a packed file detection technique based on a PE Header Analysis is proposed. In many cases, to pack and unpack the executable codes, PE files have unusual attributes in their PE headers. In this paper, these characteristics are utilized to detect the packed files. A Characteristic Vector (CV) that consists of eight elements is defined, and the Euclidean distance (ED) of the CV is calculated. The EDs of the packed files are calculated and represent the base threshold for the detection of packed files.

The various video recording device, like car black box and cctv, are used currently and video contents are used as evidence of traffic accidents and scenes of crime. To verify integrity of video content, there are various study on manipulated video content analysis. Among these studies, a study based on analysis of video file structure and its variables needs a tool which can be used to analyze file structure and extract interested attributes. In this paper, we proposed design and implementation of an analyzing tool which visualizes video file structure and its attributes. The proposed tool use a model which reflects commonality of various video container format, so it is available to analyze video structure with regardless of the video file types. And the tool specifies interested file structure properties in XML and therefore we can change target properties easily without modification of the tool.

We examine the weaknesses of the existing OOXML-based MS-Word file structure, and analyze how data concealment and forgery are performed in MS-Word digital documents. In case of forgery by including hidden information in MS-Word digital document, there is no difference in opening the file with the MS-Word Processor. However, the computer system may be malfunctioned by malware or shell code hidden in the digital document. If a malicious image file or ZIP file is hidden in the document by using the structural vulnerability of the MS-Word document, it may be infected by ransomware that encrypts the entire file on the disk even if the MS-Word file is normally executed. Therefore, it is necessary to analyze forgery and alteration of digital document through internal structure analysis of MS-Word file. In this paper, we designed and implemented a mechanism to detect this efficiently and automatic detection software, and presented a method to proactively respond to attacks such as ransomware exploiting MS-Word security vulnerabilities.

디지털 포렌식 조사에서 파일의 시간정보는 중요한 의미를 가진다. 리눅스의 Ext4(Extended File System 4)환경에서 획득할 수 있는 파일의 시간정보는 파일 접근 시간(Access Time), 수정 시간(Modification Time), Inode 변경 시간(Change Time), 삭제 시간(Deletion Time), 생성 시간(Creation Time)이다. 일반적으로 파일의 생성, 수정, 복사 등 여러 가지 행위에 따라 시간 정보가 변화되며, 증거 분석을 위해 행위에 따른 파일 시간변화에 대한 연구가 필요하다. 본 논문에서는 리눅스 Ext4 환경에서 파일 및 폴더의 다양한 행위에 따른 시간정보를 분석하였고 이를 이용하여 악성코드의 실제 감염시간과 파일의 변조 여부를 확인하는 방안을 연구하였다.

File Time information has a significant meaning in digital forensic investigation. File time information in Linux Ext4 (Extended File System 4) environment is the Access Time, Modification Time, Inode Change Time, Deletion Time and Creation Time. File time is variously changed by user manipulations such as creation, copy and edit. And, the study of file time change is necessary for evidence analysis. This study analyzes the change in time information of files or folders resulting from user manipulations in Linux operating system and analyzes ways to determine real time of malware infection and whether the file was modulation.

최근 인공지능 기술의 발전으로 모바일 환경에서 AI 응용을 수행하는 사례가 늘고 있다. 하지만, 모바일 환경은 데스크탑이나 서버에 비해 자원이 제한적이므로 인공지능 워크로드를 모바일에서 효율적으로 수행하기 위한 연구가 최 근 주목받고 있다. 대부분의 연구는 컴퓨팅 자원의 제약을 해소하기 위한 엣지 또는 클라우드로의 오프로딩에 초점이 맞추어져 있으며, 스토리지 접근과 관련한 파일 입출력 특성에 관한 연구는 아직까지 널리 이루어지지 않고 있다. 본 논문에서는 모바일 환경에서 딥러닝 애플리케이션의 실행 시 발생하는 파일 입출력 트레이스를 분석하고, 기존 모바일 워크로드와의 차이점에 대해 분석한다. 본 논문의 분석 결과가 딥러닝의 파일 접근 특성을 고려하여 미래의 스마트폰 시스템 소프트웨어를 효율적으로 설계하는 데에 활용되기를 기대한다.

Recent advancements in artificial intelligence (AI) technology have led to an increase in the implementation of AI applications in mobile environments. However, due to the limited resources in mobile devices compared to desktops and servers, there is growing interest in research aimed at efficiently executing AI workloads on mobile platforms. While most studies focus on offloading to edge or cloud solutions to mitigate computing resource constraints, research on the characteristics of file I/O related to storage access in mobile settings remains underexplored. This paper analyzes file I/O traces generated during the execution of deep learning applications in mobile environments and investigates how they differ from traditional mobile workloads. We anticipate that the findings of this study will be utilized to design future smartphone system software more efficiently, considering the file access characteristics of deep learning.

오늘날 누구나 디지털 정보를 용이하게 활용할 수 있게 됨에 따라 디지털 증거의 채택이 증가되고 있다. 하지 만 다양한 음성 파일 편집 도구를 보급과 함께 정교한 편집 과정을 거친 음성 녹음 파일의 경우 위변조 진위 여부를 판 단하는 것은 사실상 불가능하다. 본 연구는 음성 녹음 파일에 삽입, 삭제, 연결 및 합성 편집 기술을 활용해 원본 파 일과 구별하기 어려운 위변조가 가능함을 증명하고자 한다. 본 연구는 위변조 된 음성 파일을 원본과 동일한 확장자 로 인코딩하는 작업을 통해 위변조 검출의 어려움을 제시한다. 또한 특징점이 발생한 실험에 한 하여 추가적으로 천 이대역의 삭제 및 2차 인코딩 작업을 수행할 경우 위변조 검출은 불가능함을 나타냈다. 이를 통해 본 연구는 음성 녹 음 파일을 디지털 증거로 채택하기 위한 더 엄격한 증거능력 판단 기준 수립에 공헌할 것으로 기대된다.

As digital information is readily available to everyone today, the adoption of digital evidence is increasing. However, it is virtually impossible to determine the authenticity of forgery in the case of a voice recording file that has gone through a sophisticated editing process along with the spread of various voice file editing tools. This study aims to prove that forgery, which is difficult to distinguish from the original file, is possible by using insertion, deletion, linking, and synthetic editing technologies in voice recording files. This study presents the difficulty of detecting forgery by encoding a forged voice file with the same extension as the original. In addition, it was shown that forgery detection is impossible if additional transition band deletion and secondary encoding are performed only for experiments in which features occurred. Through this, this study is expected to contribute to the establishment of more stringent evidence admissibility criteria for adopting voice recording files as digital evidence.

최근 전 세계적으로 사용되는 Microsoft Office 파일에 악성코드를 삽입하는 문서형 악성코드 사례가 증가하고 있다. 문서형 악성코드는 문서 내에 악성코드를 인코딩하여 숨기는 경우가 많기 때문에 백신 프로그램을 쉽게 우회할 수 있다. 이러한 문서형 악성코드를 탐지하기 위해 먼저 Microsoft Office 파일의 형식인 OLE(Object Linking and Embedding) 파일의 구조를 분석했다. Microsoft Office에서 지원하는 기능인 VBA(Visual Basic for Applications) 매크로에 외부 프로그램을 실행시키는 쉘코드, 외부 URL에서 파일을 다운받는 URL 관련 코드 등 다수의 악성코드가 삽입된 것을 확인했다. 문서형 악성코드에서 반복적으로 등장하는 키워드 354개를 선정하였고, 각 키워드가 본문에 등장하는 횟수를 feature 로 정의했다. SVM, naïve Bayes, logistic regression, random forest 알고리즘으로 머신러닝을 수행하였으며, 각각 0.994, 0.659, 0.995, 0.998의 정확도를 보였다.

Recently, there have been many reports of document-type malicious code injecting malicious code into Microsoft Office files. Document-type malicious code is often hidden by encoding the malicious code in the document. Therefore, document-type malware can easily bypass anti-virus programs. We found that malicious code was inserted into the Visual Basic for Applications (VBA) macro, a function supported by Microsoft Office. Malicious codes such as shellcodes that run external programs and URL-related codes that download files from external URLs were identified. We selected 354 keywords repeatedly appearing in malicious Microsoft Office files and defined the number of times each keyword appears in the body of the document as a feature. We performed machine learning with SVM, naïve Bayes, logistic regression, and random forest algorithms. As a result, each algorithm showed accuracies of 0.994, 0.659, 0.995, and 0.998, respectively.

Previous works have shown the viability of NIRS technology for the prediction of fatty acids in Iberian pig fat, but although the resulting equations showed high precision, in the predictions of new samples important fluctuations were detected, greater with the time passed from calibration development to NIRS analysis. This fact makes the use of NIRS calibrations in routine analysis difficult. Moreover, this problem only appears in products like fat, that show spectrums with very defined absorption peaks at some wavelengths. This circumstance causes a high sensibility to small changes of the instrument, which are not perceived with the normal checks. To avoid these inconveniences, the software WinISI 1.04 has a mathematic algorithm that consist of create a “Repeatability File”. This file is used during calibration development to minimize the variation sources that can affect the NIRS predictions. The objective of the current work is the evaluation of the use of a repeatability file in quantitative NIRS analysis of Iberian pig fat. A total of 188 samples of Iberian pig fat, produced by COVAP, were used. NIR data were recorded using a FOSS NIRSystems 6500 I spectrophotometer equipped with a spinning module. Samples were analysed by folded transmission, using two sample cells of 0.1mm pathlength and gold surface. High accuracy calibration equations were obtained, without and with repeatability file, to determine the content of six fatty acids: miristic (SECV$\sub$without/=0.07% r$^2$$\sub$without/=0.76 and SECV$\sub$with/=0.08% r$^2$$\sub$with/=0.65), Palmitic (SECV$\sub$without/=0.28 r$^2$$\sub$without/=0.97 and SECV$\sub$with/=0.24% r$^2$$\sub$with/=0.98), palmitoleic (SECV$\sub$without/=0.08 r$^2$$\sub$without/=0.94 and SECV$\sub$with/=0.09% r$^2$$\sub$with/=0.92), Stearic (SECV$\sub$without/=0.27 r$^2$$\sub$without/=0.97 and SECV$\sub$with/=0.29% r$^2$$\sub$with/=0.96), oleic (SECV$\sub$without/=0.20 r$^2$$\sub$without/=0.99 and SECV$\sub$with/=0.20% r$^2$$\sub$with/=0.99) and linoleic (SECV$\sub$without/=0.16 r$^2$$\sub$without/=0.98 and SECV$\sub$with/=0.16% r$^2$$\sub$with/=0.98). The use of a repeatability file like a tool to reduce the variation sources that can disturbed the prediction accuracy was very effective. Although in calibration results the differences are negligible, the effect caused by the repeatability file is appreciated mainly when are predicted new samples that are not in the calibration set and whose spectrum were recorded a long time after the equation development. In this case, bias values corresponding to fatty acids predictions were lower when the repeatability file was used: miristic (bias$\sub$without/=-0.05 and bias$\sub$with/=-0.04), Palmitic (bias$\sub$without/=-0.42 and bias$\sub$with/=-0.11), Palmitoleic (bias$\sub$without/=-0.03 and bias$\sub$with/=0.03), Stearic (bias$\sub$without/=0.47 and bias$\sub$with/=0.28), oleic (bias$\sub$without/=0.14 and bias$\sub$with/=-0.04) and linoleic (bias$\sub$without/=0.25 and bias$\sub$with/=-0.20).

This paper presents the analyses of Auto CAD plot file for carving machine. The plot file is first analyzed and then sorted to properly perform a sequence of the line segments. The experimental results show that this technique can improve the carving performance, reduce the operating time, and save the tool and machine’s lifetime. In addition, this proposed technique can also be extended to apply for other coordinate machines.

With rapid increase of information requirements from various application areas, there has been much research on dynamic information storage structures that effectively support insertions, deletions and updates. In this appear we evaluate the performance of he existing dynamic signature file methods such as the S-tree, Quick Filter and HS file and provide guidelines for the most effective usage to a given operational environment. We derive analytic performance evaluation models of the storage structures based on retrieval time, storage overhead and insertion time. We also perform extensive experiments with various data distributions such as uniform, normal and exponential distributions. The relationships among various performance parameters are throughly investigated. We show through performance comparison based on analytic models and experiments that regardless of data distribution, the HS file significantly improves performance in both the retreival time and the storage overhead over S-tree and Quick Filter.

The communications link in a space program is a crucial point for upgrading its performance by handling data between spacecraft bus and payloads, because spacecraft's missions are related to the data handling mechanism using communications ports such as a controlled area network bus (CAN Bus) and a universal asynchronous receiver and transmitter (UART). The NEXTSat-1 has a lot of communications ports for performing science and technology missions. However, the top level system requirements for the NEXTSat-1 are mass and volume limitations. Normally, the communications for units shall be conducted by using point to point link which require more mass and volume to interconnect. Thus, our approach for the novel communications link in the NEXTSat-1 program is to use CAN and serializer and deserializer low voltage differential signal (SerDesLVDS) to meet the system requirements of mass and volume. The CAN Bus and SerDesLVDS were confirmed by using already defined communications link for our missions in the NEXTSat-1 program and the analysis results were reported in this study in view of data flow and size analysis.

To validate the new Evaluated Nuclear Data File $(ENDF)/B-VIII.0{\beta}4$ library, 31 different critical cores were selected and used for a benchmark test of the important parameter keff. The four utilized libraries are processed using Nuclear Data Processing Code (NJOY2016). The results obtained with the $ENDF/B-VIII.0{\beta}4$ library were compared against those calculated with ENDF/B-VI.8, ENDF/B-VII.0, and ENDF/B-VII.1 libraries using the Monte Carlo N-Particle (MCNP(X)) code. All the MCNP(X) calculations of keff values with these four libraries were compared with the experimentally measured results, which are available in the International Critically Safety Benchmark Evaluation Project. The obtained results are discussed and analyzed in this paper.

The paper analyzes a copyright owning firm's incentive to enforce its copyright in the presence of file-sharing networks. I devise a two-period model where a copyright owner sells two different versions of a creator's information good, and show that the firm's overall profits are enhanced by a strategy of differential inter-temporal enforcement of the copyright protection, compared to strategies of no enforcement or full enforcement in both periods. If the firm enforces no copyright protection in the first period, the low-valuation consumers may make and consume copies that are imperfect substitutes for the original information good. If there is a significant increase in the willingness-to-pay of some low-valuation consumers after they experience the information good, the firm can extract the increased consumer surplus by enforcing a positive level of copyright protection in the second period. Social welfare, however, is maximized in the case of no enforcement.

소프트웨어가 산업 및 사회전반으로 저변을 확대함에 따라 소프트웨어의 취약점으로 인한 위험이 증가하고 있으며, 소프트웨어의 취약점을 악용하는 사례도 빈번하게 나타나고 있다. 스마트 퍼징은 이러한 소프트웨어 취약점을 발견하기 위한 자동화된 방법이다. 그러나 스마트 퍼징을 위해서는 우선 퍼징을 수행하고자 하는 대상 소프트웨어에 대한 데이터 모델을 생성해야하며, 데이터 파일 및 소프트웨어 자체에 대한 분석이 필요하기 때문에 많은 자원이 소모된다. 따라서 효율적인 스마트 퍼징을 위해서 데이터 모델을 자동으로 생성하기 위한 방법이 필요하다. 본 논문에서는 데이터 입력 파일에 대한 분석을 통해서 스마트 퍼징을 위한 데이터 모델을 자동으로 생성하기 위한 프레임워크를 제안하고 이를 구현함으로서 소프트웨어 취약점 발견에 도움을 줄 것으로 기대한다.

The rapid expansion of the software industry has brought a serious security threat and vulnerability. Many softwares are constantly attacked by exploit codes using security vulnerabilities. Smart fuzzing is automated method to find software vulnerabilities. However, Many resources are consumed in fuzzing, because the fuzzing needs to create data model for target software and to analyze a data file and software binary. Therefore, The automated method for efficient smart fuzzing is needed to develop the automated data model. In this paper, through analysing the input file format and optimizing the data structure, we propose an efficient data modeling framework for smart fuzzing and implement the framework for detect software vulnerabilities.