• Title/Summary/Keyword: 컴퓨터 기반 오류 분석

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Security Verification of Wireless Remote Control System Using CPN (CPN을 이용한 무선원격제어시스템의 안전성 검증)

  • 이문구
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.13 no.5
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    • pp.81-90
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    • 2003
  • Existing web-based system management software solutions show some limitations in time and space. Moreover, they possess such as shortcomings unreliable error message announcements and difficulties with real-time assistance suppers and emergency measures. In order to solve these deficiencies, Wireless Remote Control System was designed and implemented. Wireless Remote Control System is able to manage and monitor remote systems by using mobile communication devices for instantaneous control. The implementation of Wireless Remote Control System leads to these security Problems as well as solutions to aforementioned issues with existing web-based system management software solutions. Therefore, this paper has focused on the security matters related to Wireless Remote Control System. The designed security functions include mobile device user authentication and target system access control. For security verification of these security functions introduced CPN(Coloured Petri Nets) which is capable of expressing every possible state for each stage. And then in this paper was verified its security through PI(Place Invariant) based on CPN(Coloured Petri Nets). The CPN expression and analysis method of the proposed security function can also be a useful method for analyzing other services in the future.

Blockchain Based Data-Preserving AI Learning Environment Model for Cyber Security System (AI 사이버보안 체계를 위한 블록체인 기반의 Data-Preserving AI 학습환경 모델)

  • Kim, Inkyung;Park, Namje
    • The Journal of Korean Institute of Information Technology
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    • v.17 no.12
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    • pp.125-134
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    • 2019
  • As the limitations of the passive recognition domain, which is not guaranteed transparency of the operation process, AI technology has a vulnerability that depends on the data. Human error is inherent because raw data for artificial intelligence learning must be processed and inspected manually to secure data quality for the advancement of AI learning. In this study, we examine the necessity of learning data management before machine learning by analyzing inaccurate cases of AI learning data and cyber security attack method through the approach from cyber security perspective. In order to verify the learning data integrity, this paper presents the direction of data-preserving artificial intelligence system, a blockchain-based learning data environment model. The proposed method is expected to prevent the threats such as cyber attack and data corruption in providing and using data in the open network for data processing and raw data collection.

Side-Channel Attacks on Square Always Exponentiation Algorithm (Square Always 멱승 알고리듬에 대한 부채널 공격)

  • Jung, Seung-Gyo;Ha, Jae-Cheol
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.24 no.3
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    • pp.477-489
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    • 2014
  • Based on some flaws occurred for implementing a public key cryptosystem in the embedded security device, many side-channel attacks to extract the secret private key have been tried. In spite of the fact that the cryptographic exponentiation is basically composed of a sequence of multiplications and squarings, a new Square Always exponentiation algorithm was recently presented as a countermeasure against side-channel attacks based on trading multiplications for squarings. In this paper, we propose Known Power Collision Analysis and modified Doubling attacks to break the Right-to-Left Square Always exponentiation algorithm which is known resistant to the existing side-channel attacks. And we also present a Collision-based Combined Attack which is a combinational method of fault attack and power collision analysis. Furthermore, we verify that the Square Always algorithm is vulnerable to the proposed side-channel attacks using computer simulation.

ANIDS(Advanced Network Based Intrusion Detection System) Design Using Association Rule Mining (연관법칙 마이닝(Association Rule Mining)을 이용한 ANIDS (Advanced Network Based IDS) 설계)

  • Jeong, Eun-Hee;Lee, Byung-Kwan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.11 no.12
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    • pp.2287-2297
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    • 2007
  • The proposed ANIDS(Advanced Network Intrusion Detection System) which is network-based IDS using Association Rule Mining, collects the packets on the network, analyze the associations of the packets, generates the pattern graph by using the highly associated packets using Association Rule Mining, and detects the intrusion by using the generated pattern graph. ANIDS consists of PMM(Packet Management Module) collecting and managing packets, PGGM(Pattern Graph Generate Module) generating pattern graphs, and IDM(Intrusion Detection Module) detecting intrusions. Specially, PGGM finds the candidate packets of Association Rule large than $Sup_{min}$ using Apriori algorithm, measures the Confidence of Association Rule, and generates pattern graph of association rules large than $Conf_{min}$. ANIDS reduces the false positive by using pattern graph even before finalizing the new pattern graph, the pattern graph which is being generated is compared with the existing one stored in DB. If they are the same, we can estimate it is an intrusion. Therefore, this paper can reduce the speed of intrusion detection and the false positive and increase the detection ratio of intrusion.

Solitary Work Detection of Heavy Equipment Using Computer Vision (컴퓨터비전을 활용한 건설현장 중장비의 단독작업 자동 인식 모델 개발)

  • Jeong, Insoo;Kim, Jinwoo;Chi, Seokho;Roh, Myungil;Biggs, Herbert
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.41 no.4
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    • pp.441-447
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    • 2021
  • Construction sites are complex and dangerous because heavy equipment and workers perform various operations simultaneously within limited working areas. Solitary works of heavy equipment in complex job sites can cause fatal accidents, and thus they should interact with spotters and obtain information about surrounding environments during operations. Recently, many computer vision technologies have been developed to automatically monitor construction equipment and detect their interactions with other resources. However, previous methods did not take into account the interactions between equipment and spotters, which is crucial for identifying solitary works of heavy equipment. To address the drawback, this research develops a computer vision-based solitary work detection model that considers interactive operations between heavy equipment and spotters. To validate the proposed model, the research team performed experiments using image data collected from actual construction sites. The results showed that the model was able to detect workers and equipment with 83.4 % accuracy, classify workers and spotters with 84.2 % accuracy, and analyze the equipment-to-spotter interactions with 95.1 % accuracy. The findings of this study can be used to automate manual operation monitoring of heavy equipment and reduce the time and costs required for on-site safety management.

A Study on the Research Model for the Standardization of Software-Similarity-Appraisal Techniques (소프트웨어 복제도 감정기법의 표준화 모델에 관한 연구)

  • Bahng, Hyo-Keun;Cha, Tae-Own;Chung, Tai-Myoung
    • The KIPS Transactions:PartD
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    • v.13D no.6 s.109
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    • pp.823-832
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    • 2006
  • The Purpose of Similarity(Reproduction) Degree Appraisal is to determine the equality or similarity between two programs and it is a system that presents the technical grounds of judgment which is necessary to support the resolution of software intellectual property rights through expert eyes. The most important things in proceeding software appraisal are not to make too much of expert's own subjective judgment and to acquire the accurate-appraisal results. However, up to now standard research and development for its systematic techniques are not properly made out and as different expert as each one could approach in a thousand different ways, even the techniques for software appraisal types have not exactly been presented yet. Moreover, in the analyzing results of all the appraisal cases finished before, through a practical way, we blow that there are some damages on objectivity and accuracy in some parts of the appraisal results owing to the problems of existing appraisal procedures and techniques or lack of expert's professional knowledge. In this paper we present the model for the standardization of software-similarity-appraisal techniques and objective-evaluation methods for decreasing a tolerance that could make different results according to each expert in the same-evaluation points. Especially, it analyzes and evaluates the techniques from various points of view concerning the standard appraisal process, setting a range of appraisal, setting appraisal domains and items in detail, based on unit processes, setting the weight of each object to be appraised, and the degree of logical and physical similarity, based on effective solutions to practical problems of existing appraisal techniques and their objective and quantitative standardization. Consequently, we believe that the model for the standardization of software-similarity-appraisal techniques will minimizes the possibility of mistakes due to an expert's subjective judgment as well as it will offer a tool for improving objectivity and reliability of the appraisal results.

Theory and Implementation of Dynamic Taint Analysis for Tracing Tainted Data of Programs (프로그램의 오염 정보 추적을 위한 동적 오염 분석의 이론 및 구현)

  • Lim, Hyun-Il
    • KIPS Transactions on Computer and Communication Systems
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    • v.2 no.7
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    • pp.303-310
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    • 2013
  • As the role of software increases in computing environments, issues in software security become more important problems. Dynamic taint analysis is a technique to trace and manage tainted data originated from unreliable sources during the execution of a program. This analysis can be applied to software security verification as well as software behavior understanding, testing unexpected errors, or debugging. In the previous researches, they focussed only to show the analysis results of dynamic taint analysis, and they did not logically describe propagation process of tainted data and analysis procedures. So, there were difficulties in understanding the analysis procedures or applying to other analysis. In this paper, by theoretically describing the analysis procedure, we logically show how the propagation process of tainted data can be traced, and present a theoretical model for dynamic taint analysis. In addition, we verify the correctness of the proposed model by implementing an analyser, and show that propagation of tainted data can be traced by the model. The proposed model can be applied to understand the analysis procedures of data flows in dynamic taint analysis, and can be used as an base knowledge for designing and implementing analysis method, which applies such analysis method.

Short-term Mortality Prediction of Recurrence Patients with ST-segment Elevation Myocardial Infarction (ST 분절 급상승 심근경색 환자들의 단기 재발 사망 예측)

  • Lim, Kwang-Hyeon;Ryu, Kwang-Sun;Park, Soo-Ho;Shon, Ho-Sun;Ryu, Keun-Ho
    • Journal of the Korea Society of Computer and Information
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    • v.17 no.10
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    • pp.145-154
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    • 2012
  • Recently, the cardiovascular disease has increased by causes such as westernization dietary life, smoking, and obesity. In particular, the acute myocardial infarction (AMI) occupies 50% death rate in cardiovascular disease. Following this trend, the AMI has been carried out a research for discovery of risk factors based on national data. However, there is a lack of diagnosis minor suitable for Korean. The objective of this paper is to develop a classifier for short-term relapse mortality prediction of cardiovascular disease patient based on prognosis data which is supported by KAMIR(Korea Acute Myocardial Infarction). Through this study, we came to a conclusion that ANN is the most suitable method for predicting the short-term relapse mortality of patients who have ST-segment elevation myocardial infarction. Also, data set obtained by logistic regression analysis performed highly efficient performance than existing data set. So, it is expect to contribute to prognosis estimation through proper classification of high-risk patients.

Automated Data Extraction from Unstructured Geotechnical Report based on AI and Text-mining Techniques (AI 및 텍스트 마이닝 기법을 활용한 지반조사보고서 데이터 추출 자동화)

  • Park, Jimin;Seo, Wanhyuk;Seo, Dong-Hee;Yun, Tae-Sup
    • Journal of the Korean Geotechnical Society
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    • v.40 no.4
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    • pp.69-79
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    • 2024
  • Field geotechnical data are obtained from various field and laboratory tests and are documented in geotechnical investigation reports. For efficient design and construction, digitizing these geotechnical parameters is essential. However, current practices involve manual data entry, which is time-consuming, labor-intensive, and prone to errors. Thus, this study proposes an automatic data extraction method from geotechnical investigation reports using image-based deep learning models and text-mining techniques. A deep-learning-based page classification model and a text-searching algorithm were employed to classify geotechnical investigation report pages with 100% accuracy. Computer vision algorithms were utilized to identify valid data regions within report pages, and text analysis was used to match and extract the corresponding geotechnical data. The proposed model was validated using a dataset of 205 geotechnical investigation reports, achieving an average data extraction accuracy of 93.0%. Finally, a user-interface-based program was developed to enhance the practical application of the extraction model. It allowed users to upload PDF files of geotechnical investigation reports, automatically analyze these reports, and extract and edit data. This approach is expected to improve the efficiency and accuracy of digitizing geotechnical investigation reports and building geotechnical databases.

A Hybrid Blockchain-Based E-Voting System with BaaS (BaaS를 이용한 하이브리드 블록체인 기반 전자투표 시스템)

  • Kang Myung Joe;Kim Mi Hui
    • KIPS Transactions on Computer and Communication Systems
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    • v.12 no.8
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    • pp.253-262
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    • 2023
  • E-voting is a concept that includes actions such as kiosk voting at a designated place and internet voting at an unspecified place, and has emerged to alleviate the problem of consuming a lot of resources and costs when conducting offline voting. Using E-voting has many advantages over existing voting systems, such as increased efficiency in voting and ballot counting, reduced costs, increased voting rate, and reduced errors. However, centralized E-voting has not received attention in public elections and voting on corporate agendas because the results of voting cannot be trusted due to concerns about data forgery and modulation and hacking by others. In order to solve this problem, recently, by designing an E-voting system using blockchain, research has been actively conducted to supplement concepts lacking in existing E-voting, such as increasing the reliability of voting information and securing transparency. In this paper, we proposed an electronic voting system that introduced hybrid blockchain that uses public and private blockchains in convergence. A hybrid blockchain can solve the problem of slow transaction processing speed, expensive fee by using a private blockchain, and can supplement for the lack of transparency and data integrity of transactions through a public blockchain. In addition, the proposed system is implemented as BaaS to ensure the ease of type conversion and scalability of blockchain and to provide powerful computing power. BaaS is an abbreviation of Blockchain as a Service, which is one of the cloud computing technologies and means a service that provides a blockchain platform ans software through the internet. In this paper, in order to evaluate the feasibility, the proposed system and domestic and foreign electronic voting-related studies are compared and analyzed in terms of blockchain type, anonymity, verification process, smart contract, performance, and scalability.