• 융합보안논문지
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• 제6권3호
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• pp.87-95
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• 2006

침입은 컴퓨터 자원의 무결성, 기밀성, 유효성을 저해하고 컴퓨터 시스템의 보안정책을 파괴하는 일련의 행위의 집합이다. 이러한 침입을 탐지하는 침입탐지시스템은 데이터 수집, 데이터의 가공 및 축약, 침입 분석 및 탐지 그리고 보고 및 대응의 4 단계로 구성되어진다. 침입탐지시스템의 방대한 데이터가 수집된 후, 침입을 효율적으로 탐지하기 위해서는 특징 선택이 중요하다. 이 논문에서 유전자 알고리즘과 결정트리를 활용한 특징 선택 방법을 제안한다. 또한 KDD 데이터에서 실험을 통해 방법의 유효성을 검증한다.

• 대한원격탐사학회지
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• 제38권6_2호
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• pp.1633-1641
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• 2022

도시의 인구 집중과 무분별한 개발은 대기오염, 열섬현상과 같은 다양한 환경 문제들을 유발하며, 자연재해로 인한 피해 상황을 악화시키는 등 인재의 원인이 되고 있다. 도심 수목은 이러한 도시 문제들의 해결방안으로 제시되어왔으며, 실제로 환경 개선 기능을 제공하는 등 중요한 역할들을 수행한다. 이에 따라 수목이 도시 환경에 미치는 영향을 파악하기 위해 도심 수목에서 개별목에 대한 정량적인 측정 및 분석이 요구된다. 그러나 도심 수목의 복잡성 및 다양성은 단일 수목 탐지 정확도를 낮추는 문제점이 존재한다. 따라서 본 연구는 수목 개체에 대해 효과적인 탐지가 가능한 고해상도 항공영상 및 object detection에서 뛰어난 성능을 발휘한 You Only Look Once Version 5 (YOLOv5) 모델을 사용하여 도심 수목을 효과적으로 탐지하는 연구를 진행하였다. 수목 AI 학습 데이터셋의 구축을 위한 라벨링 가이드라인을 생성하고 이를 기준으로 동작구 수목에 대해 box annotation을 수행하였다. 구축된 데이터셋으로부터 다양한 scale의 YOLOv5 모델들을 테스트하고 최적의 모델을 채택하여 효율적인 도심 수목 탐지를 수행한 결과, mean Average Precision (mAP) 0.663의 유의미한 결과를 도출하였다.

• 한국조명전기설비학회지:조명전기설비
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• 제7권4호
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• pp.26-33
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• 1993

트리열화 현상을 감지하기 위한 노력으로 음향방출 펄스를 계측할 수 있는 자동계측 시스템을 자체 개발하였다.가교 폴리에틸렌(이하 XLPE라 부른다)시편에 교번전압 15[kV]을 인가했을 때 수초형 트리(bush-type tree)로의 개시 및 진전시 수초형에서 가지형(branch-type tree)으로 진전할 때는 음향방출 평균펄스 진폭(이하 펄스평균 진폭이라 부른다)이 작은 펄스가 많이 발생하였고, 가지형에서 수초형 트리로 진전할 때에는 큰 펄스진폭을 갖는 작은 수의 펄스수를 계측할 수 있다. 이로써 작은 진폭을 갖는 많은 펄스진폭 및 펄스수에 대한 3차원의 분포양상으로서 왜도(Skewness)가 방전의 특성량임을 확인하였고, 왜도를 이용한 S-평면상의 궤적(Trajectory) 변화시 원점에서 멀어질수록 파괴에 가까워짐을 알았다.

• Steel and Composite Structures
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• 제19권3호
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• pp.569-583
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• 2015

Recent years, explosive welding structures have been widely used in many engineering fields. The bonding state detection of explosive welding structures is significant to prevent unscheduled failures and even catastrophic accidents. However, this task still faces challenges due to the complexity of the bonding interface. In this paper, a new method called dual-tree complex wavelet transform based permutation entropy (DTCWT-PE) is proposed to detect bonding state of such structures. Benefiting from the complex analytical wavelet function, the dual-tree complex wavelet transform (DTCWT) has better shift invariance and reduced spectral aliasing compared with the traditional wavelet transform. All those characters are good for characterizing the vibration response signals. Furthermore, as a statistical measure, permutation entropy (PE) quantifies the complexity of non-stationary signals through phase space reconstruction, and thus it can be used as a viable tool to detect the change of bonding state. In order to more accurate identification and detection of bonding state, PE values derived from DTCWT coefficients are proposed to extract the state information from the vibration response signal of explosive welding structure, and then the extracted PE values serve as input vectors of support vector machine (SVM) to identify the bonding state of the structure. The experiments on bonding state detection of explosive welding pipes are presented to illustrate the feasibility and effectiveness of the proposed method.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.186-192
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• 2023

Malwares are becoming a major problem nowadays all around the world in android operating systems. The malware is a piece of software developed for harming or exploiting certain other hardware as well as software. The term Malware is also known as malicious software which is utilized to define Trojans, viruses, as well as other kinds of spyware. There have been developed many kinds of techniques for protecting the android operating systems from malware during the last decade. However, the existing techniques have numerous drawbacks such as accuracy to detect the type of malware in real-time in a quick manner for protecting the android operating systems. In this article, the authors developed a hybrid model for android malware detection using a decision tree and KNN (k-nearest neighbours) technique. First, Dalvik opcode, as well as real opcode, was pulled out by using the reverse procedure of the android software. Secondly, eigenvectors of sampling were produced by utilizing the n-gram model. Our suggested hybrid model efficiently combines KNN along with the decision tree for effective detection of the android malware in real-time. The outcome of the proposed scheme illustrates that the proposed hybrid model is better in terms of the accurate detection of any kind of malware from the Android operating system in a fast and accurate manner. In this experiment, 815 sample size was selected for the normal samples and the 3268-sample size was selected for the malicious samples. Our proposed hybrid model provides pragmatic values of the parameters namely precision, ACC along with the Recall, and F1 such as 0.93, 0.98, 0.96, and 0.99 along with 0.94, 0.99, 0.93, and 0.99 respectively. In the future, there are vital possibilities to carry out more research in this field to develop new methods for Android malware detection.

• Journal of Information Processing Systems
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• 제18권5호
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• pp.677-687
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• 2022

We propose a detection algorithm based on tree-structured convolutional neural networks (TsCNNs) that finds pornography, propaganda, or other inappropriate content on a social media network. The algorithm sequentially applies the typical convolutional neural network (CNN) algorithm in a tree-like structure to minimize classification errors in similar classes, and thus improves accuracy. We implemented the detection system and conducted experiments on a data set comprised of 6 ordinary classes and 11 inappropriate classes collected from the Korean military social network. Each model of the proposed algorithm was trained, and the performance was then evaluated according to the images and videos identified. Experimental results with 20,005 new images showed that the overall accuracy in image identification achieved a high-performance level of 99.51%, and the effectiveness of the algorithm reduced identification errors by the typical CNN algorithm by 64.87 %. By reducing false alarms in video identification from the domain, the TsCNNs achieved optimal performance of 98.11% when using 10 minutes frame-sampling intervals. This indicates that classification through proper sampling contributes to the reduction of computational burden and false alarms.

• 정보처리학회논문지C
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• 제14C권1호
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• pp.1-6
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• 2007

안전한 네트워크의 운영을 함에 있어 네트워크 침입 탐지에서 오탐지율을 줄이고 정탐지율을 높이는 것은 매우 중요한 일이라 할 수 있다. 최근에 얼굴 인식과 생물학 정보칩 분류 등에서 활발히 적용 연구되는 SVM을 침입탐지에 이용하면 실시간 탐지가 가능하므로 탐지율의 향상을 기대할 수 있다. 그러나 기존의 연구에서는 입력값들을 벡터공간에 나타낸 후 계산된 값을 근거로 분류하므로, 이산형의 데이터는 입력 정보로 사용할 수 없다는 단점을 가지고 있다. 따라서 이 논문에서는 의사결정트리를 SVM에 결합시킨 침입 탐지 모델을 제안하고 이에 대한 성능을 평가한 결과 기존 방식에 비해 침입 탐지율, F-P오류율, F-N오류율에 있어 각각 5.5%, 0.16%, 0.82% 향상이 있음을 보였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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• pp.185-188
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• 2002

This paper proposes a efficient way to use Database that is constructed about attack-pattern. For IDS that activate confrontation, we reconstruct by Layered Attack Tree after constructing attack pattern by Attack Tree. And then this paper has designed IDS that Layered Attack Tree is applied, verified them.

• 디지털산업정보학회논문지
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• 제8권2호
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• pp.59-65
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• 2012

Emergency medical information center performs role of medical direction about disease consult and pre-hospital emergency handling scheme work to people. Emergency medical information system plays a major role to be decreased mortality and disability of emergency patient by providing information of medical institution especially when emergency patient has appeared. But, various attacks as a hacking have been happened in Emergency medical information system recently. In this paper, we proposed security structure which can protect the system securely by detecting attacks from outside effectively. Intrusion detection was performed using rule based detection technique according to protocol for every packet to detect attack and intrusion was reported to control center if intrusion was detected also. Intrusion detection was performed again using decision tree for packet which intrusion detection was not done. We experimented effectiveness using attacks as TCP-SYN, UDP flooding and ICMP flooding for proposed security structure in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4005-4025
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• 2019

Copy-move forgery (CMF) in digital images is a detrimental tampering of artefacts that requires precise detection and analysis. CMF is performed by copying and pasting a part of an image into other portions of it. Despite several efforts to detect CMF, accurate identification of noise, blur and rotated region-mediated forged image areas is still difficult. A novel algorithm is developed on the basis of quaternion polar complex exponential transform (QPCET) to detect CMF and is conducted involving a few steps. Firstly, the suspicious image is divided into overlapping blocks. Secondly, invariant features for each block are extracted using QPCET. Thirdly, the duplicated image blocks are determined using k-dimensional tree (kd-tree) block matching. Lastly, a new technique is introduced to reduce the flat region-mediated false matches. Experiments are performed on numerous images selected from the CoMoFoD database. MATLAB 2017b is used to employ the proposed method. Metrics such as correct and false detection ratios are utilised to evaluate the performance of the proposed CMF detection method. Experimental results demonstrate the precise and efficient CMF detection capacity of the proposed approach even under image distortion including rotation, scaling, additive noise, blurring, brightness, colour reduction and JPEG compression. Furthermore, our method can solve the false match problem and outperform existing ones in terms of precision and false positive rate. The proposed approach may serve as a basis for accurate digital image forensic investigations.