• 제목/요약/키워드: Cloud Detection

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R2NET: Storage and Analysis of Attack Behavior Patterns

  • M.R., Amal;P., Venkadesh
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제17권2호
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    • pp.295-311
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    • 2023
  • Cloud computing has evolved significantly, intending to provide users with fast, dependable, and low-cost services. With its development, malicious users have become increasingly capable of attacking both its internal and external security. To ensure the security of cloud services, encryption, authorization, firewalls, and intrusion detection systems have been employed. However, these single monitoring agents, are complex, time-consuming, and they do not detect ransomware and zero-day vulnerabilities on their own. An innovative Record and Replay-based hybrid Honeynet (R2NET) system has been developed to address this issue. Combining honeynet with Record and Replay (RR) technology, the system allows fine-grained analysis by delaying time-consuming analysis to the replay step. In addition, a machine learning algorithm is utilized to cluster the logs of attackers and store them in a database. So, the accessing time for analyzing the attack may be reduced which in turn increases the efficiency of the proposed framework. The R2NET framework is compared with existing methods such as EEHH net, HoneyDoc, Honeynet system, and AHDS. The proposed system achieves 7.60%, 9.78%%, 18.47%, and 31.52% more accuracy than EEHH net, HoneyDoc, Honeynet system, and AHDS methods.

TeraScan시스템에서 NOAA/AVHRR 해수면온도 산출시 구름 영향에 따른 신뢰도 부여 기법: 5월 자료 적용 (Generation of Sea Surface Temperature Products Considering Cloud Effects Using NOAA/AVHRR Data in the TeraScan System: Case Study for May Data)

  • 양성수;양찬수;박광순
    • 한국해양환경ㆍ에너지학회지
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    • 제13권3호
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    • pp.165-173
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    • 2010
  • 본 연구에서는 TeraScan시스템에서 산출되는 NOAA/AVHRR 해수면온도(SST) 자료의 신뢰도를 부여하기 위한 방법으로 구름 영향 정도를 단계별로 나누는 방법을 소개한다. TeraScan시스템에서 구름탐지는 주간과 야간에 따라 다른 파라미터와 경계값을 사용한다. 주간 구름탐지에서는 채널 2번(가시채널)과 4번(적외채널)을 이용하며, 채널 4번 휘도온도의 공간일관성(ch4_delta)과 채널 2번 알베도의 공간일관성(ch2_delta) 및 알베도 경계값(ch2_max) 검사를 수행한다. 야간의 경우, 가시채널을 사용할 수 없기 때문에 채널 3번(단파적외채널)과 4번(적외채널)을 사용하여 각 화소에 대한 차이값(ch3_minus_ch4)을 비교하고, 채널 4번 휘도온도 공간일관성(ch4_delta) 및 경계값(min_ch4_temp) 평가가 이루어진다. 여기서는 주야에 따른 변화를 보기 위해 2009년 5월 13일 00시 48분(UTC)과 21시 00분(UTC) 에 수신된 자료를 사용했다. TeraScan시스템을 통해 총 6가지 경계치를 검토했고, ch4_delta는 우리나라 주변 수온 전선에서 발생하는 구름 탐지 오류가 발생하지 않는 값으로 주야간 각각 5와 3.5로 결정되었다. 주간 파라미터로 사용되는 ch2_delta는 여러 값에 대한 적용 결과 2로, ch2_max는 3부터 8까지의 범위가 적절한 것으로 나타났다. 야간에 사용되는 ch3_minus_ch4는 -2부터 2까지의 범위, min_ch4_temp는 0으로 결정되었다. 즉, 구름의 영향 정도는 주간 ch2_max와 야간 ch3_minus_ch4의 경계값을 4 단계로 나눠 해수면온도자료를 산출하였다. 본 연구에서 사용한 경계값은 5월 자료에 대해 설정된 값이며, 향후 한반도 주변 해역의 특성과 시간별, 공간별, 계절별로 적절한 경계값을 설정하는 연구가 장기적으로 필요하며, 위의 특성들을 감안한 자료동화용 SST 생산프로세스 정립 및 결과분석 연구가 필요하다.

Fundamental Research on Spring Season Daytime Sea Fog Detection Using MODIS in the Yellow Sea

  • Jeon, Joo-Young;Kim, Sun-Hwa;Yang, Chan-Su
    • 대한원격탐사학회지
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    • 제32권4호
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    • pp.339-351
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    • 2016
  • For the safety of sea, it is important to monitor sea fog, one of the dangerous meteorological phenomena which cause marine accidents. To detect and monitor sea fog, Moderate Resolution Imaging Spectroradiometer (MODIS) data which is capable to provide spatial distribution of sea fog has been used. The previous automatic sea fog detection algorithms were focused on detecting sea fog using Terra/MODIS only. The improved algorithm is based on the sea fog detection algorithm by Wu and Li (2014) and it is applicable to both Terra and Aqua MODIS data. We have focused on detecting spring season sea fog events in the Yellow Sea. The algorithm includes application of cloud mask product, the Normalized Difference Snow Index (NDSI), the STandard Deviation test using infrared channel ($STD_{IR}$) with various window size, Temperature Difference Index(TDI) in the algorithm (BTCT - SST) and Normalized Water Vapor Index (NWVI). Through the calculation of the Hanssen-Kuiper Skill Score (KSS) using sea fog manual detection result, we derived more suitable threshold for each index. The adjusted threshold is expected to bring higher accuracy of sea fog detection for spring season daytime sea fog detection using MODIS in the Yellow Sea.

Sensitive method for the detection of Apple scar skin viroid(ASSVd) by nested reverse transcription-polymerase chain reaction

  • Lee, Sung-Joon;Kim, Chung;Sim, Sang-Mi;Lee, Dong-Hyuk;Lee, Jai-Youl
    • 한국식물병리학회:학술대회논문집
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    • 한국식물병리학회 2003년도 정기총회 및 추계학술발표회
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    • pp.143.2-143
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    • 2003
  • A rapid and sensitive assay for the specific detection of plant viroids using reverse transcription-polymerase chain reaction(RT-PCR) has been developed already. The nested RT-PCR assay cloud be applied for the detection of apple scar skin viroid(ASSVd) from young leaves and other tissues. ASSVd has central conserved region(CCR), terminal left(T$\sub$L/) and terminal right(T$\sub$R/) domain. Primers were designed from these regions. Primer sets were successfully applicable for the amplification of full length or partial region of ASSVd by nested RT-PCR. Nested RT-PCR assay was more sensitive and accurate method to detect ASSVd from young trees during the early time of apple cultivation.

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휴머노이드 로봇의 움직임 생성을 위한 장애물 인식방법 (Obstacle Detection for Generating the Motion of Humanoid Robot)

  • 박찬수;김도익
    • 제어로봇시스템학회논문지
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    • 제18권12호
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    • pp.1115-1121
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    • 2012
  • This paper proposes a method to extract accurate plane of an object in unstructured environment for a humanoid robot by using a laser scanner. By panning and tilting 2D laser scanner installed on the head of a humanoid robot, 3D depth map of unstructured environment is generated. After generating the 3D depth map around a robot, the proposed plane extraction method is applied to the 3D depth map. By using the hierarchical clustering method, points on the same plane are extracted from the point cloud in the 3D depth map. After segmenting the plane from the point cloud, dimensions of the planes are calculated. The accuracy of the extracted plane is evaluated with experimental results, which show the effectiveness of the proposed method to extract planes around a humanoid robot in unstructured environment.

Scalable Big Data Pipeline for Video Stream Analytics Over Commodity Hardware

  • Ayub, Umer;Ahsan, Syed M.;Qureshi, Shavez M.
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제16권4호
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    • pp.1146-1165
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    • 2022
  • A huge amount of data in the form of videos and images is being produced owning to advancements in sensor technology. Use of low performance commodity hardware coupled with resource heavy image processing and analyzing approaches to infer and extract actionable insights from this data poses a bottleneck for timely decision making. Current approach of GPU assisted and cloud-based architecture video analysis techniques give significant performance gain, but its usage is constrained by financial considerations and extremely complex architecture level details. In this paper we propose a data pipeline system that uses open-source tools such as Apache Spark, Kafka and OpenCV running over commodity hardware for video stream processing and image processing in a distributed environment. Experimental results show that our proposed approach eliminates the need of GPU based hardware and cloud computing infrastructure to achieve efficient video steam processing for face detection with increased throughput, scalability and better performance.

KoBERT를 활용한 실시간 보이스피싱 탐지기법 개념설계 (Design of Real-Time Voice Phishing Detection Techniques using KoBERT)

  • 김영진;이병엽;강아름
    • 한국컴퓨터정보학회:학술대회논문집
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    • 한국컴퓨터정보학회 2024년도 제69차 동계학술대회논문집 32권1호
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    • pp.95-96
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    • 2024
  • 본 논문은 금융 범죄 중 하나인 보이스피싱을 실시간으로 예방하기 위한 탐지 기법을 제안한다. 제안된 모델은 수화기에 출력되는 음성을 녹음하고 네이버 CSR(Cloud Speech Recognition)을 통해 텍스트 파일로 변환한 후 딥러닝 기반의 KoBERT를 바탕으로 다양한 보이스피싱 패턴을 학습하여 실시간 환경에서의 신속하고 정확한 탐지를 위해 실제 통화 데이터를 적절하게 처리하여, 이를 통해 효과적인 보이스피싱 예방에 도움을 줄 것으로 예상된다.

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유효 경보를 위한 새로운 낙뢰 경보시스템의 개발 방법에 대한 제안 (A Proposal on the Development Method of a New Lightning Warning System for Effective Alerts)

  • 심해섭;이복희
    • 조명전기설비학회논문지
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    • 제29권12호
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    • pp.68-76
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    • 2015
  • We examine the standalone lightning warning system (LWS) and its warning performances for three years. This system acquires and analyzes the data of cloud-to-ground strike (CG), intra-cloud discharge (IC) and electrostatic field (EF) to produce prior warnings with respect to the impending arrival of CG in the area of concern (AOC). The warnings in this system are carried out based on the fixed two areas method. To evaluate warning performances, we analyzed the statistics of warnings with probability of detection (POD) and false alarm ratio (FAR). Based on the previous study, we revised the trigger and clear conditions of lightning warning for improving the performances of the system. As a result of this revision, POD increased from 0.18 to 0.44 and FAR decreased from 0.96 to 0.78 during the summer of 2014. However, the LWS was not possible to trigger effective alerts (EA) because there was no effective lead time (LT) for the fixed two areas method. Problems related to the low detection efficiency of IC and the use of EF data for warnings still decreased POD and increased FAR. Hence, we proposed the development method of a new LWS (NLWS) that would be composed of integrated weather data, the flexible two areas and the user software in order to trigger EA and improve warning performances.

A Remote Sensed Data Combined Method for Sea Fog Detection

  • Heo, Ki-Young;Kim, Jae-Hwan;Shim, Jae-Seol;Ha, Kyung-Ja;Suh, Ae-Sook;Oh, Hyun-Mi;Min, Se-Yun
    • 대한원격탐사학회지
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    • 제24권1호
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    • pp.1-16
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    • 2008
  • Steam and advection fogs are frequently observed in the Yellow Sea from March to July except for May. This study uses remote sensing (RS) data for the monitoring of sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided a valuable information for the occurrence of steam and advection fogs as a ground truth. The RS data used in this study were GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and shortwave IR channel of GOES-9 and MTSAT-1R satellites was applied to detect sea fog. The results showed that DCD, texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind data was used to provide the wind speed criteria for a fog event. The laplacian computation was designed for a measurement of the homogeneity. A new combined method, which includes DCD, QuikSCAT wind speed and laplacian computation, was applied to the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and laplacian are -2.0 K, $8m\;s^{-1}$ and 0.1, respectively. The validation results showed that the new combined method slightly improves the detection of sea fog compared to DCD method: improvements of the new combined method are $5{\sim}6%$ increases in the Heidke skill score, 10% decreases in the probability of false detection, and $30{\sim}40%$ increases in the odd ratio.

클라우드 환경에서 Log4J 취약점 분석을 통한 공격 탐지 기술 (Attack Detection Technology through Log4J Vulnerability Analysis in Cloud Environments)

  • 변정연;이상희;유채연;박원형
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2022년도 춘계학술대회
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    • pp.557-559
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    • 2022
  • 오픈소스의 사용으로 개발 환경이 편리해지고 유지보수가 보다 용이해진 장점이 있지만 보안적인 측면에서 볼 때 취약점에 노출되기 쉽다는 한계점이 존재한다. 이와 관련하여 최근에는 아파치에서 매우 광범위하게 사용되고 있는 오픈소스 로깅 라이브러리인 LOG4J 취약점이 발견되었다. 현재 이 취약점의 위험도는 '최고' 수준이며 개발자들도 이와 같은 문제점을 인지하지 못한 채 많은 시스템에 사용하고 있어 향후 LOG4J 취약점으로 인한 해킹 사고가 지속적으로 발생할 우려가 있다. 본 논문에서는 클라우드 환경에서 LOG4J 취약점에 대해서 자세하게 분석하고, 보안관제시스템에서 보다 신속하고 정확하게 취약점을 탐지할 수 있는 SNORT 탐지 정책 기술을 제안한다. 이를 통해 향후 보안 관련 입문자, 보안 담당자 그리고 기업들이 LOG4J 취약점 사태에 대비하여 효율적인 모니터링 운영과 신속하고 능동적인 대처가 가능해질 것으로 기대 한다.

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