• 정보보호학회논문지
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• 제29권4호
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• pp.775-784
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• 2019

신규 및 변종 악성코드의 발생으로 모바일, IoT, windows, mac 등 여러 환경에서 악성코드 침해 공격이 지속적으로 증가하고 있으며, 시그니처 기반 탐지의 대응만으로는 악성코드 탐지에 한계가 존재한다. 또한, 난독화, 패킹, Anti-VM 기법의 적용으로 분석 성능이 저하되고 있는 실정이다. 이에 유사성 해시 기반의 패턴 탐지 기술과 패킹에 따른 파일 분류 후의 정적 분석 적용으로 기계학습 기반 악성코드 식별이 가능한 시스템을 제안한다. 이는 기존에 알려진 악성코드의 식별에 강한 패턴 기반 탐지와 신규 및 변종 악성코드 탐지에 유리한 기계학습 기반 식별 기술을 모두 활용하여 보다 효율적인 탐지가 가능하다. 본 연구 결과물은 정보보호 R&D 데이터 챌린지 2018 대회의 AI기반 악성코드 탐지 트랙에서 제공하는 정상파일과 악성코드를 대상으로 95.79% 이상의 탐지정확도를 도출하여 분석 성능을 확인하였다. 향후 지속적인 연구를 통해 패킹된 파일의 특성에 맞는 feature vector와 탐지기법을 추가 적용하여 탐지 성능을 높이는 시스템 구축이 가능할 것으로 기대한다.

• Journal of Electrical Engineering and information Science
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• 제3권1호
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• pp.73-79
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• 1998

In this paper, a new traffic sign detection algorithm.. and a symbol recognition algorithm are proposed. For traffic sign detection, a dominant color transform is introduced, which serves as a tool of highlighting a dominant primary color, while discarding the other two primary colors. For symbol recognition, the curvilinear shape distribution on a circle centered on the centroid of symbol, called a circular pattern vector, is used as a spatial feature of symbol. The circular pattern vector is invariant to scaling, translation, and rotation. As simulation results, the effectiveness of traffic sign detection and recognition algorithms are confirmed, and it is shown that group of circular patter vectors based on concentric circles is more effective than circular pattern vector of a single circle for a given equivalent number of elements of vectors.

• 한국소음진동공학회논문집
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• 제16권1호
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• pp.27-33
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• 2006

Fault diagnosis and condition monitoring for rotating machines are important for efficiency and accident prevention. The process of fault diagnosis is to extract the feature of signals and to classify each state. Conventionally, fault diagnosis has been developed by combining signal processing techniques for spectral analysis and pattern recognition, however these methods are not able to diagnose correctly for certain rotating machines and some faulty phenomena. In this paper, we add a minimum detection error algorithm to the previous method to reduce detection error rate. Vibration signals of the induction motor are measured and divided into subband signals. Each subband signal is processed to obtain the RMS, standard deviation and the statistic data for constructing the feature extraction vectors. We make a study of the fault diagnosis system that the feature extraction vectors are applied to K-means clustering algorithm and minimum detection error algorithm.

• 한국정밀공학회지
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• 제15권6호
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• pp.167-174
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• 1998

The ultrasonic test is recognized for its significance as a non-destructive testing method to detect volume defects such as porosity and incomplete penetration which reduce strength in the weld zone. This paper illustrates the defect detection in the weld zone of ferritic carbon steel using ultrasonic wave and the evaluation of pattern recognition by chaotic feature extraction using time series signal of detected defects as data. Shown in the time series data were that the time delay was 4 and the embedding dimension was 6 which indicate the geometric dimension of the subject system and the extent of information correlation. Based on fractal dimension and lyapunov exponent in quantitative chaotic feature extraction, feature value of 2.15, 0.47 is presented for porosity and 2.24, 0.51 for incomplete penetration The precision rate of the pattern recognition is enhanced with these values on the total waveform of defect signal in the weld zone. Therefore, we think that the ultrasonic pattern recognition method of weld zone defects of ferritic carbon steel by ultrasonic-chaotic feature extraction proposed in this paper can boost precision rate further than the existing method applying only partial waveform.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.141-149
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• 2023

Repacked mobile apps constitute about 78% of all malware of Android, and it greatly affects the technical ecosystem of Android. Although many methods exist for repacked app detection, most of them suffer from performance issues. In this manuscript, a novel method using the Constant Key Point Selection and Limited Binary Pattern (CKPS: LBP) Feature extraction-based Hashing is proposed for the identification of repacked android applications through the visual similarity, which is a notable feature of repacked applications. The results from the experiment prove that the proposed method can effectively detect the apps that are similar visually even that are even under the double fold content manipulations. From the experimental analysis, it proved that the proposed CKPS: LBP method has a better efficiency of detecting 1354 similar applications from a repository of 95124 applications and also the computational time was 0.91 seconds within which a user could get the decision of whether the app repacked. The overall efficiency of the proposed algorithm is 41% greater than the average of other methods, and the time complexity is found to have been reduced by 31%. The collision probability of the Hashes was 41% better than the average value of the other state of the art methods.

• 대한원격탐사학회지
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• 제30권4호
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• pp.537-546
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• 2014

본 논문은 비 접근 지역에 존재하는 관심물체의 위치를 고해상도 광학 위성영상을 이용하여 찾아내기 위한 방법을 제안한다. 관심물체는 정확하게 규정된 크기와 모양을 갖는 것이 아니라, 개념적으로 유사한 패턴을 가진 물체들의 집합이다. 본 논문에서는 유사 객체 검색에서 Histogram of Gradients (HOG) feature를 이용하여 입력 영상의 관심물체의 특징을 추출하고, 추출된 특징 데이터를 이용하여 다른 영상들의 관심물체를 탐색하는 Support Vector Machine (SVM) 학습 및 분류기를 개발하였다. 제안한 방법은 관심물체를 자동으로 찾아줌으로써, 넓은 영역에서 수동으로 관심물체를 탐색하는데 소요되는 시간과 노력을 줄일 수 있는 효과가 있음을 확인하였다.

• 한국지능시스템학회논문지
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• 제26권2호
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• pp.120-126
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• 2016

본 연구에서는 HCbCr 색 특징과 RBFNNs 패턴분류기를 이용하여 얼굴영상을 효과적으로 검출하고 인식하기 위한 방법에 대해 제안한다. 피부색을 검출하는 것은 계산이 빠르고 형태 변형에 강인하여 얼굴을 검출하기에 유용하지만 유사한 색을 갖는 다른 물체를 잘못 검출하기도 한다. 따라서 피부색 검출의 정확도를 높이기 위하여 HSI 색공간과 YCbCr 색공간으로부터 각각 H요소와 CbCr요소를 추출하고 이를 결합하는 방법을 제안하였다. 그리고 각각의 피부색 후보 영역에 대하여 Haar-like 특징을 사용하여 눈을 검출함으로써 얼굴의 정확한 위치를 찾아냈다. 마지막으로 제안된 FCM 기반 RBFNNs 패턴분류기를 이용하여 얼굴 인식을 수행하였다. 또 Cambridge ICPR 영상 DB에 대하여 제안된 방법의 모의실험을 수행하고 그 결과를 제시하였다.

• 한국멀티미디어학회논문지
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• 제18권4호
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• pp.443-451
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• 2015

In this paper we suggest a new two-wheelers detection algorithm using uniform local binary pattern weighting value for projection vectors. The first, we calculate feature vectors using projection method which has robustness for rotation invariant and reducing dimensionality for each cell from origin image. The second, we applied new weighting values which are calculated by the modified local binary pattern showing the fast compute and simple to implement. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• Journal of the Optical Society of Korea
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• 제17권2호
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• pp.188-199
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• 2013

This paper presents a novel algorithm for nighttime detection of the lane markers painted on a road at night. First of all, the proposed algorithm uses neighborhood average filtering, 8-directional Sobel operator and thresholding segmentation based on OTSU's to handle raw lane images taken from a digital CCD camera. Secondly, combining intensity map and gradient map, we analyze the distribution features of pixels on boundaries of lanes in the nighttime and construct 4 feature sets for these points, which are helpful to supply with sufficient data related to lane boundaries to detect lane markers much more robustly. Then, the searching method in multiple directions- horizontal, vertical and diagonal directions, is conducted to eliminate the noise points on lane boundaries. Adapted Hough transformation is utilized to obtain the feature parameters related to the lane edge. The proposed algorithm can not only significantly improve detection performance for the lane marker, but it requires less computational power. Finally, the algorithm is proved to be reliable and robust in lane detection in a nighttime scenario.

• Journal of Multimedia Information System
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• 제4권2호
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• pp.79-82
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• 2017

Smoke and fire have different shapes and colours. This article suggests a fully connected system which is used two features using Adaboost algorithm for constructing a strong classifier as linear combination. We calculate the local histogram feature by gradient and bin, local binary pattern value, and projection vectors for each cell. According to the histogram magnitude, this paper applied adapted weighting value to improve the recognition rate. To preserve the local region and shape feature which has edge intensity, this paper processed the normalization sequence. For the extracted features, this paper Adaboost algorithm which makes strong classification to classify the objects. Our smoke detection system based on the proposed approach leads to higher detection accuracy than other system.