• International Journal of Computer Science & Network Security
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• 제23권2호
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• pp.199-202
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• 2023

Carriage return (CR) and line feed (LF), also known as CRLF injection is a type of vulnerability that allows a hacker to enter special characters into a web application, altering its operation or confusing the administrator. Log poisoning and HTTP response splitting are two prominent harmful uses of this technique. Additionally, CRLF injection can be used by an attacker to exploit other vulnerabilities, such as cross-site scripting (XSS). Email injection, also known as email header injection, is another way that can be used to modify the behavior of emails. The Open Web Application Security Project (OWASP) is an organization that studies vulnerabilities and ranks them based on their level of risk. According to OWASP, CRLF vulnerabilities are among the top 10 vulnerabilities and are a type of injection attack. Automated testing can help to quickly identify CRLF vulnerabilities, and is particularly useful for companies to test their applications before releasing them. However, CRLF vulnerabilities can also lead to the discovery of other high-risk vulnerabilities, and it fosters a better approach to mitigate CRLF vulnerabilities in the early stage and help secure applications against known vulnerabilities. Although there has been a significant amount of research on other types of injection attacks, such as Structure Query Language Injection (SQL Injection). There has been less research on CRLF vulnerabilities and how to detect them with automated testing. There is room for further research to be done on this subject matter in order to develop creative solutions to problems. It will also help to reduce false positive alerts by checking the header response of each request. Security automation is an important issue for companies trying to protect themselves against security threats. Automated alerts from security systems can provide a quicker and more accurate understanding of potential vulnerabilities and can help to reduce false positive alerts. Despite the extensive research on various types of vulnerabilities in web applications, CRLF vulnerabilities have only recently been included in the research. Utilizing automated testing as a recurring task can assist companies in receiving consistent updates about their systems and enhance their security.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권5호
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• pp.161-168
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• 2021

균열은 건물, 교량, 도로, 수송관 등의 기반시설의 안전성에 영향을 주는 요소이다. 본 연구에서는 검사 비용과 시간을 줄일 수 있는 자동화된 균열 탐지 시스템을 다룬다. 환경과 표면에 강건한 시스템을 구성하기 위해서, 본 연구에서는 여러 사전 연구에서 사용된 다양한 표면의 균열 데이터 셋을 수집하여 통합 데이터 셋을 구축하였다. 이후, 컴퓨터 비전 분야에 높은 성능을 발휘하는 VGG, ResNet, WideResNet, ResNeXt, DenseNet, EfficientNet 딥러닝 모델을 적용하였다. 통합 데이터 셋은 훈련 집합(80%)과 테스트 집합(20%)으로 나누어 모델 성능을 검증하기 위해서 사용했다. 실험 결과, DenseNet121 모델이 높은 마라미터 효율성을 가지면서도 테스트 집합에 대해 96.20%의 정확도를 달성하여 가장 높은 성능을 보여주었다. 딥러닝 모델의 균열 검출 성능 검증을 통해, DenseNet121를 활용하여 컴퓨팅 자원이 적은 소형 디바이스에서도 높은 균열 검출 성능을 보이는 탐지 시스템을 구축이 가능함을 확인했다.

• 한국지리정보학회지
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• 제22권3호
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• pp.146-154
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• 2019

현재까지 많은 노력을 통해 정밀도로지도를 구축하고 활용하기 위한 시스템을 개발하여 적용 중에 있으며, 최근 도로변화에 대한 신속 변화 및 지도 갱신 시스템 개발을 통해 정밀도로 지도의 갱신을 신속히 하기 위한 노력을 기울이고 있다. 정밀도로지도는 자율주행 안전을 위해 지도의 무결성 및 정확성이 요구되어지며, 이를 위해 국토지리정보원(2018)에서는 검사 방법을 만들어 확인하고 있다. 마찬가지로 갱신된 정밀도로지도 품질을 확보할 수 있도록 관련 기술의 기준 및 평가방법이 필요하다. 이에 본 논문에서는 자율주행을 위한 도로변화 신속 탐지 및 갱신기술을 분석하고 통합 품질 검증을 위한 평가기준과 항목을 선정하였다. 평가 항목은 위치정확도와 판독정확도로 정하고, 선정한 평가기준을 바탕으로 실시간 변화탐지 및 정밀지도의 갱신 기술에 대한 평가방법을 제시하였다. 향후 본 연구 결과를 통해 자율차의 안전주행을 지원하는 정밀도로지도의 품질확보에 기여할 수 있을 것으로 기대한다.

• International Journal of Internet, Broadcasting and Communication
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• 제10권4호
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• pp.70-74
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• 2018

In order to ensure the flow of goods available and more flexible, reduce labor costs, many factories and industrial zones around the world are gradually moving to use automated solutions. One of them is to use Automated guided vehicles (AGV). Currently, there are a line tracing method as an AGV operating method, and a method of estimating the current position of the AGV and matching with a factory map and knowing the moving direction of the AGV. In this paper, we propose ceiling Light and guided line based moving direction estimation algorithm using multi-camera on the AGV in smart factory that can operate stable AGV by compensating the disadvantages of existing AGV operation method. The proposed algorithm is able to estimate its position and direction using a general - purpose camera instead of a sensor. Based on this, it can correct its movement error and estimate its own movement path.

• Journal of information and communication convergence engineering
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• 제15권4호
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• pp.250-255
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• 2017

In this paper, we propose a method of detecting the region for measuring intima-media thickness (IMT). The existing methods for IMT measurement are automatic, but the region used for measuring IMT is not detected automatically but often set by the user. Therefore, research on detecting the intima-media region is needed for fully automated IMT measurement. The proposed method uses a morphological feature of the carotid artery visible as two long high-brightness horizontal lines at the upper and lower parts. It uses Gaussian blurring, ends-in search stretching, color quantization using a color-importance-based self-organizing map, and morphological operations to emphasize and to detect the morphological feature. The experimental results for evaluating the performance of the proposed method showed a 97.25% (106/109) success rate. Therefore, the proposed method can be used to develop a fully automated IMT measurement system.

• ETRI Journal
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• 제33권2호
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• pp.259-266
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• 2011

We present a new method for an automated markerless system to describe, analyze, and classify human gait motion. The automated system consists of three stages: I) detection and extraction of the moving human body and its contour from image sequences, ii) extraction of gait figures by the joint angles and body points, and iii) analysis of motion parameters and feature extraction for classifying human gait. A sequential set of 2D stick figures is used to represent the human gait motion, and the features based on motion parameters are determined from the sequence of extracted gait figures. Then, a k-nearest neighbor classifier is used to classify the gait patterns. In experiments, this provides an alternative estimate of biomechanical parameters on a large population of subjects, suggesting that the estimate of variance by marker-based techniques appeared generous. This is a very effective and well-defined representation method for analyzing the gait motion. As such, the markerless approach confirms uniqueness of the gait as earlier studies and encourages further development along these lines.

• Korean Journal of Radiology
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• 제21권1호
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• pp.15-24
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• 2020

Mammography is the primary screening method for breast cancers. However, the sensitivity of mammographic screening is lower for dense breasts, which are an independent risk factor for breast cancers. Automated breast ultrasound (ABUS) is used as an adjunct to mammography for screening breast cancers in asymptomatic women with dense breasts. It is an effective screening modality with diagnostic accuracy comparable to that of handheld ultrasound (HHUS). Radiologists should be familiar with the unique display mode, imaging features, and artifacts in ABUS, which differ from those in HHUS. The purpose of this study was to provide a comprehensive review of the clinical significance of dense breasts and ABUS screening, describe the unique features of ABUS, and introduce the method of use and interpretation of ABUS.

• Asian Pacific Journal of Cancer Prevention
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• 제15권21호
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• pp.9101-9105
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• 2014

Background: The purpose of this study was to compare the accuracy and effectiveness of automated breast volume scanning (ABVS) and hand-held ultrasound (HHUS) in the detection of breast cancer in a large population group with a long-term follow-up, and to investigate whether different ultrasound systems may influence the estimation of cancer detection. Materials and Methods: Institutional review board approval was obtained for this retrospective study, and informed consent was waived. From September 2010 to August 2011, a total of 1,866 ABVS and 3,700 HHUS participants, who underwent these procedures at our institute, were included in this study. Cancers occurring during the study and subsequent follow-up were evaluated. The reference standard was a combination of histology and follow-up imaging (${\geq}12months$). The recall rate, cancer detection yield, diagnostic accuracy, sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values were calculated with exact 95% confidence intervals. Results: The recall rate was 2.57 per 1,000 (48/1,866) for ABVS and 3.57 per 1,000 (132/3,700) for HHUS, with a significant difference (p=0.048). The cancer detection yield was 3.8 per 1,000 for ABVS and 2.7 per 1,000 for HHUS. The diagnostic accuracy was 97.7% for ABVS and 96.5% for HHUS with statistical significance (p=0.018). The specificity of ABVS and HHUS were 97.8%, 96.7%, respectively (p=0.022). Conclusions: ABVS shows a comparable diagnostic performance to HHUS. ABVS is an effective supplemental tool for mammography in breast cancer detection in a large population.

• 반도체디스플레이기술학회지
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• 제12권2호
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• pp.57-61
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• 2013

This paper presents adaptive neuro-fuzzy inference based defect detection method for various defect types, such as micro-crack, fingerprint and contamination, in heterogeneously textured surface of polycrystalline solar wafers. Polycrystalline solar wafer consists of various crystals so the surface of solar wafer shows heterogeneously textures. Because of this property the visual inspection of defects is very difficult. In the proposed method, we use local binary feature and fuzzy reasoning for defect detection. Experimental results show that our proposed method achieves a detection rate of 80%~100%, a missing rate of 0%~20% and an over detection (overkill) rate of 9%~21%.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1999년도 춘계종합학술대회
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• pp.203-207
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• 1999

본 논문에서는 연관 마이닝 기법을 이용한 침입 시나리오 자동생성 알고리즘을 제안한다. 현재 알려진 침입 탐지는 크게 비정상 탐지(Anomaly Detection)와 오용 탐지(Misuse Detection)로 분류되는데, 침입 판정을 위해 전자는 통계적 방법, 특징 추출, 신경망 기법 둥을 사용하며, 후자는 조건부 확률, 전문가 시스템, 상태 전이 분석, 패턴 매칭 둥을 사용한다. 기존에 제안된 침입 탐지 알고리즘들의 경우 알려지지 않은 침입은 보안 전문가에 의해 수동적으로 시나리오를 생성ㆍ갱신한다. 본 알고리즘은 기존의 데이터 내에 있는 알려지지 않은 유효하고 잠재적으로 유용한 정보를 발견하는데 사용되는 연관 마이닝 알고리즘을 상태전이 기법에 적용하여 침입 시나리오를 자동으로 생성한다. 본 논문에서 제안한 알고리즘은 보안 전문가에 의해 수동적으로 생성되던 침입 시나리오를 자동적으로 생성할 수 있으며, 기존 알고리즘에 비해서 새로운 침입에 대응하는 것이 용이하고 시스템 유지 보수비용이 적다는 이점이 있다.