• Convergence Security Journal
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• v.20 no.1
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• pp.77-84
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• 2020

Due to the lack of a system that can detect/identify and strike North Korea's small unmanned aerial vehicles, no immediate response was made. In order to solve the problem of response, we should prepare for threats by developing weapons systems that can be immediately hit when identifying small-sized unmanned aerial vehicle detection radar and creating specialized research organizations for research and development of equipment.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.1
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• pp.99-107
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• 2014

Abruptly occurred obstacles on highway threaten driving safety. Radar draws the attention to the collision avoidance system because it can be fully operational in all weather, and day and night condition. This paper presents the design, implementation and performance test results of pulsed Doppler radar system for detection and warning of road obstacles. The system is designed to consider highway environment and detection capability about various fixed and moving obstacles. The system consists of 4 subsystems, which include antenna unit, transmitter and receiver unit, radar signal & data processing unit, and controller & display unit. The core technologies include clutter map based change detection for fixed obstacles detection, Doppler estimation for velocity detection of moving targets, and azimuth angle estimation method using monopulse for lane estimation and tracking. The design performance of the developed radar system is verified through experiments using a fixed reference target and moving vehicles in test highway.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.537-544
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• 2020

Advances in detection techniques, such as mutation and obfuscation, are being advanced with the development of malware technology. In the malware detection technology, unknown malware detection technology is important, and a method for Malware Authorship Attribution that detects an unknown malicious code by identifying the author through distributed malware is being studied. In this paper, we try to extract the compiler information affecting the binary-based author identification method and to investigate the sensitivity of feature selection, probability and non-probability models, and optimization to classification efficiency between studies. In the experiment, the feature selection method through information gain and the support vector machine, which is a non-probability model, showed high efficiency. Among the optimization studies, high classification accuracy was obtained through feature selection and model optimization through the proposed framework, and resulted in 48% feature reduction and 53 faster execution speed. Through this study, we can confirm the sensitivity of feature selection, model, and optimization methods to classification efficiency.

• The Journal of the Acoustical Society of Korea
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• v.19 no.8
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• pp.78-83
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• 2000

Passive sonar system is designed to classify the underwater targets by analyzing and comparing the various acoustic characteristics such as signal strength, bandwidth, number of tonals and relationship of tonals from the extracted tonals and frequency lines. First of all the precise detection and extraction of signal frequency lines is of particular importance for enhancing the reliability of target classification. But, the narrowband frequency lines which are the line formed in spectrogram by a tonal of constant frequency in each frame can be detected weakly or discontinuously because of the variation of signal strength and transmission loss in the sea. Also, it is very difficult to detect and extract precisely the signal frequency lines by the complexity of impulsive ambient noise and signal components. In this paper, the automatic detection and extraction method that can detect and extract the signal components of frequency tines precisely are proposed. The proposed method can be applied under the bad conditions with weak signal strength and high ambient noise. It is confirmed by the simulation using real underwater target data.

• Review of KIISC
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• v.32 no.4
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• pp.61-69
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• 2022

C/C++에는 다수의 메모리 취약점이 존재하며 ASan은 낮은 오버헤드와 높은 탐지율로 이러한 메모리 취약점을 탐지하기 위해 광범위하게 사용되고 있다. 그러나 상용 프로그램 중 다수는 메모리를 효율적으로 사용하기 위해 Custom Memory Allocator(CMA)를 구현하여 사용하며, ASan은 이러한 CMA로부터 파생된 버그를 대부분 탐지하지 못한다. 이를 극복하기 위해 본 연구에서는 LLVM IR 코드를 RNN 신경망에 학습하여 CMA를 탐지하고, ASan이 CMA를 식별할 수 있도록 수정하여 CMA로부터 파생된 메모리 취약점을 탐지할 수 있는 도구인 CMASan을 제안한다. ASan과 CMASan의 성능 및 CMA 관련 취약점의 탐지 결과를 비교·분석하여 CMASan이 낮은 실행시간 및 적은 메모리 오버헤드로 ASan이 탐지하지 못하는 메모리 취약점을 탐지할 수 있음을 확인하였다.

• Annual Conference of KIPS
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• 2017.11a
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• pp.735-737
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• 2017

본 논문에서는 이유자돈의 건강에 심각한 문제를 발생시키고, 농가의 생산성을 급격하게 저하시키는 돼지 호흡기 질환을 효과적으로 식별하는 시스템을 제안한다. 제안된 시스템은 먼저, 돼지가 내는 소리에서 스펙트로그램 정보를 추출한다. 추출된 정보는 최근 각광을 받고 있는 딥러닝 기법 중 하나인 CNN에 적용되어, 효과적인 특징으로 변환된 후 돼지 호흡기 질환을 탐지 및 식별한다. 세종시에 위치한 돼지농장에서 취득한 실제 소리 데이터 셋을 이용하여 본 논문에서 제안하는 소리 센서 환경에서의 돼지 호흡기 질병 탐지 시스템의 성능을 실험적으로 검증한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.3
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• pp.121-129
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• 2008

This paper presents vulnerability identification method based on meaning which is making use of the concept of atomic vulnerability. Also, we are making use of decomposition and specialization processes which were used in DEVS/SES to get identifiers. This vulnerability representation method is useful for managing and removing vulnerability in organized way. It is helpful to make a relation between vulnerability assessing and intrusion detection rules in lower level. The relation enables security manager to response more quickly and conveniently. Especially, this paper shows a mapping between Nessus plugins and Snort rules using meaning based vulnerability identification method and lists usages based on three goals that security officer keeps in mind about vulnerability. The contribution of this work is in suggestion of meaning based vulnerability identification method and showing the cases of its usage for the rule integration of vulnerability assessment and intrusion detection.

• Annual Conference of KIPS
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• 2019.05a
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• pp.394-397
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• 2019

센서 및 정보 통신 기술의 발전은 산업 현장에서 취득한 정보를 기반으로 다양한 연구를 수행할 수 있는 토대가 되었다. 본 연구에서는 철도의 진로 방향을 전환하는 선로 전환기 주변에 설치한 소리 센서에서 수집한 소리를 기반으로 선로 전환기의 이상 상황을 탐지하고자 한다. 이와 같은 소리 데이터 기반의 이상 상황 탐지 시스템을 실제 산업 현장에서 성공적으로 운용되기 위해서는 소리 취득 시 발생하는 다양한 잡음 환경에서도 이상 상황을 식별할 수 있는 강인함이 보장되어야 한다. 본 논문에서는 소리 음질을 향상시키기 위하여 SEGAN(Speech Enhancement Generative Adversarial Network)을 활용하며, CNN(Convolutional Neural Network)을 기반으로 선로 전환기의 이상 상황을 식별하는 시스템을 제안한다. 수집된 소리 데이터를 기반으로 제안한 시스템을 실험적으로 검증한 바 잡음에 강인한 성능을 확인하였다.

• Annual Conference of KIPS
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• 2010.11a
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• pp.592-595
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• 2010

본 논문에서는 감시카메라 환경에서 취득한 오디오 데이터를 입력으로 하여, 비정상 상황을 인식하는 시스템을 제안한다. 제안된 시스템은 단일클래스 SVM의 대표적인 모델인 SVDD와 최근 얼굴 인식 분야에서 성공적인 업적을 보여주고 있는 신호 처리 분야의 SRC를 계층적으로 결합한 구조로써, 첫 번째 계층에서는 SVDD로 비정상 소리를 신속하게 탐지하여 관리자에게 알람 경고하고, 두 번째 계층의 SRC는 탐지된 비정상 소리를 유형별로 세분화 식별하여 관리자에게 비상 상황을 보고함으로써 관리자의 위기 상황 대처를 돕는다. 제안된 시스템은 실시간 처리가 가능하며, 점증적 갱신의 학습 능력으로 인하여 비정상 오디오 데이터베이스의 변화에도 능동적으로 적응할 수 있다. 실험을 통하여 제안된 시스템의 성능을 검증한다.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.91-98
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• 2018

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. In particular, porcine respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this paper, we propose a noise-robust system for the early detection and recognition of pig wasting diseases using sound data. In this method, first we convert one-dimensional sound signals to two-dimensional gray-level images by normalization, and extract texture images by means of dominant neighborhood structure technique. Lastly, the texture features are then used as inputs of convolutional neural networks as an early anomaly detector and a respiratory disease classifier. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (low-cost sound sensor) and accurately (over 96% accuracy) even under noise-environmental conditions, either as a standalone solution or to complement known methods to obtain a more accurate solution.