• 대한교통학회지
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• 제32권5호
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• pp.497-502
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• 2014

공항에서의 지상교통관제는 매우 중요한 항공교통업무 가운데 하나이다. 따라서 세계 각국에서는 이와 관련된 시뮬레이터의 개발에 대한 관심이 지대하다. 본 연구는 우리나라에서 개발 및 고도화된 비행장관제시뮬레이터에 대한 특징과 장점 및 향후 개발되는 A-SMGCS와의 연동성을 기술하였다. 본 장비의 특징은 훈련관제사의 단독적인 훈련이 가능하도록 설계되었으며 다양한 기상조건 및 정상 및 비정상상황 설정으로 상황에 부합되는 관제사 교육훈련이 가능하다는 점이다. 따라서 본 비행장관제시뮬레이터를 통하여 항공관제 부분에서의 종합훈련은 물론 다양한 상황에서의 관제사 훈련을 통하여 비행안전과 공항용량 증대 훈련에 크게 기여를 할 것으로 사료된다.

• 자동차안전학회지
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• 제10권3호
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• pp.32-37
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• 2018

Recently, the driver can be assisted by the advanced active safety devices such as ADAS from road traffic risks. With this system, driver and passenger may freed from can driving tasks or kept eyes on forward direction while on the road. Help from adoptive cruise control, auto parking and newly develped automated driving vehicles technologies, the driver positions will vary significantly from the current standard driver position during the travel time. On this hypothesis, the objective of this study is analyze the behavior and injuries of drivers in the event of frontal impact under these abnormal driver position. Based on the KNCAP frontal impact testing method, this simulation matrix was set-up with dummies of 5 th tile female Hybrid III dummy and 50 th tile male Hybrid III dummy. The small sedan type passenger car was modeled in this simulation. The series of simulation was performed to compare the injuries and behaviour of each dummy, varying the seating status and seat position of each dummy.

• 전기학회논문지
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• 제58권11호
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• pp.2281-2285
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• 2009

Powerline communication is the data signal which is modulated by carrier frequency through the installed powerline at in-home or office is transmitted and received signals are separated into data signal with using band-pass filter which cent-frequency is carrier frequency. The home gateway, an equipment which works as an gateway for ubiquitous home network, relays all functions of a home network. The home gateway must always be connected in order to provide seamless services. However it gives unfavorable power consumption. Therefore the needs for working in maximum power saving mode while there is no data traffic and for invoking to the normal function when it is necessary. So, in this paper we survey the development of low power PLC modem monitoring of power consumption and breaking abnormal power in the home Network.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.2-4
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• 2023

Sequence data plays an important role in the field of intelligence, especially for industrial control, traffic control and other aspects. Finding abnormal parts in sequence data has long been an application field of AI technology. In this paper, we propose an anomaly detection method for sequence data using a diffusion model. The diffusion model has two major advantages: interpretability derived from rigorous mathematical derivation and unrestricted selection of backbone models. This method uses the diffusion model to predict and reconstruct the sequence data, and then detects the abnormal part by comparing with the real data. This paper successfully verifies the feasibility of the diffusion model in the field of anomaly detection. We use the combination of MLP and diffusion model to generate data and compare the generated data with real data to detect anomalous points.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권11호
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• pp.449-456
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• 2021

침입 탐지 시스템(IDS: Intrusion Detection System)은 보안을 침해하는 이상 행위를 탐지하는 기술로서 비정상적인 조작을 탐지하고 시스템 공격을 방지한다. 기존의 침입탐지 시스템은 트래픽 패턴을 통계 기반으로 분석하여 설계하였다. 그러나 급속도로 성장하는 기술에 의해 현대의 시스템은 다양한 트래픽을 생성하기 때문에 기존의 방법은 한계점이 명확해졌다. 이런 한계점을 극복하기 위해 다양한 기계학습 기법을 적용한 침입탐지 방법의 연구가 활발히 진행되고 있다. 본 논문에서는 다양한 네트워크 환경의 트래픽을 시뮬레이션 장비에서 생성한 NGIDS-DS(Next Generation IDS Dataset)를 이용하여 이상(Anomaly) 탐지 정확도를 높일 수 있는 데이터 전처리 기법에 관한 비교 연구를 진행하였다. 데이터 전처리로 패딩(Padding)과 슬라이딩 윈도우(Sliding Window)를 사용하였고, 정상 데이터 비율과 이상 데이터 비율의 불균형 문제를 해결하기 위해 AAE(Adversarial Auto-Encoder)를 적용한 오버샘플링 기법 등을 적용하였다. 또한, 전처리된 시퀀스 데이터의 특징벡터를 추출할 수 있는 Word2Vec 기법 중 Skip-gram을 이용하여 탐지 정확도의 성능 향상을 확인하였다. 비교실험을 위한 모델로는 PCA-SVM과 GRU를 사용하였고, 실험 결과는 슬라이딩 윈도우, Skip-gram, AAE, GRU를 적용하였을 때, 더 좋은 성능을 보였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.224-227
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• 2003

The Intrsuion Detecion Systems(IDS) are required the accuracy, the adaptability, and the expansion in the information society to be changed quickly. Also, it is required the more structured, and intelligent IDS to protect the resource which is important and maintains a secret in the complicated network environment. The research has the purpose to build the model for the intelligent IDS, which creates the intrusion patterns. The intrusion pattern has extracted from the vast amount of data. To manage the large size of data accurately and efficiently, the link analysis and sequence analysis among the data mining techniqes are used to build the model creating the intrusion patterns. The model is consist of "Time based Traffic Model", "Host based Traffic Model", and "Content Model", which is produced the different intrusion patterns with each model. The model can be created the stable patterns efficiently. That is, we can build the intrusion detection model based on the intelligent systems. The rules prodeuced by the model become the rule to be represented the intrusion data, and classify the normal and abnormal users. The data to be used are KDD audit data.

• Journal of Communications and Networks
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• 제10권1호
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• pp.89-97
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• 2008

Anomaly detection systems playa significant role in protection mechanism against attacks launched on a network. The greatest challenge in designing systems detecting anomalous exploits is defining what to measure. Effective yet simple, Shannon entropy metrics have been successfully used to detect specific types of malicious traffic in a number of commercially available IDS's. We believe that Renyi entropy measures can also adequately describe the characteristics of a network as a whole as well as detect abnormal traces in the observed traffic. In addition, Renyi entropy metrics might boost sensitivity of the methods when disambiguating certain anomalous patterns. In this paper we describe our efforts to understand how Renyi mutual information can be applied to anomaly detection as an offline computation. An initial analysis has been performed to determine how well fast spreading worms (Slammer, Code Red, and Welchia) can be detected using our technique. We use both synthetic and real data audits to illustrate the potentials of our method and provide a tentative explanation of the results.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2003년도 추계학술대회 논문집(III)
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• pp.145-150
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• 2003

The national subway went under construction in 1971, and after three years of endeavor, Seoul subway line number one opened for traffic in 1974. Line number two went under construction in 1978 and it opened for traffic in 1984. With the use of safety operation for more than 20 years, the life cycle nearly came to an end. Therefore the improvements for the safety operation are unavoidable. The total system should not be affected when the new and conventional systems are overlapped, the system operation is in the initial stage, and it confronts the situation of abnormal operation. However, there is a total lack of experience in construction and improvement for the trains that are in the use of large transport and density headway. In this paper, we propose an improvement method of the subway signalling system using ATO (Automatic Train Operation control scheme) to which the latest Digital ATC is applied, and examine the first application model of ATO system.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• 한국통신학회논문지
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• 제37B권11호
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• pp.1082-1089
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• 2012

최근 사이버 공간에서는 대규모 사이버 공격들을 위해 봇넷(Botnet)을 형성하여 자산 손실과 같은 경제적 위협뿐만 아니라 Stuxnet과 같은 국가적으로 위협이 되고 있다. 진화된 봇넷은 DNS(Domain Name System)를 악용하여 C&C 서버와 좀비간의 통신 수단으로 사용하고 있다. DNS는 인터넷에서의 주요 인프라이고, 무선 인터넷의 대중화로 지속적으로 DNS 트래픽이 증가되고 있다. 반면에, 도메인 주소를 이용한 공격들도 증가되고 있는 현실이다. 본 논문에서는 지도 학습 기반의 데이터 분류 기술을 이용한 DNS 트래픽 기반의 사이버 위협 도메인 탐지 기술에 대해 연구한다. 더불어, 개발된 DNS 트래픽을 이용한 사이버위협 도메인 탐지 시스템은 대용량의 DNS데이터를 수집, 분석, 정상/비정상 도메인 분류 기능을 제공한다.