• Journal of IKEEE
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• v.27 no.4
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• pp.531-536
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• 2023

This paper proposes a sliding window method with frame feature insertion for immediate attack detection on in-vehicle networks. This method guarantees real-time attack detection by labeling based on the attack status of the current frame. Experiments show that the proposed method improves detection performance by giving more weight to the current frame in CNN computation. The proposed model was designed based on a lightweight LeNet-5 architecture and it achieves 100% detection for DoS attacks. Additionally, by comparing the complexity with conventional models, the proposed model has been proven to be more suitable for resource-constrained devices like ECUs.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.355-364
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• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.2
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• pp.193-200
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• 2011

A BOR(Boil-Off Rate) prediction model for the NO96 membrane-type LNG insulation containment filled with superlite powders during laden voyage is presented in this paper. Finite element model for the unsteady-state heat transfer analysis is constructed by considering the air and water conditions and by employing the homogenization method to simplify the complex insulation material composition. BOR is evaluated in terms of the total amount of heat invaded into LNGCC and its variation to the major variables is investigated by the parametric heat transfer analysis. Based upon the parametric results, a BOR prediction model which is in function of the LNG tank size, the insulation layer thickness and the powder thermal conductivity is derived. Through the verification experiment, the accuracy of the derived prediction model is justified such that the maximum relative difference is less than 1% when compared with the direct numerical estimation using the FEM analysis.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.47-53
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• 2022

Recently, with the change of the intelligent security paradigm, study to apply various information generated from various information security systems to AI-based anomaly detection is increasing. Therefore, in this study, in order to convert log-like time series data into a vector, which is a numerical feature, the CBOW and Skip-gram inference methods of deep learning-based Word2Vec model and statistical method based on the coincidence frequency were used to transform the published ADFA system call data. In relation to this, an experiment was carried out through conversion into various embedding vectors considering the dimension of vector, the length of sequence, and the window size. In addition, the performance of the embedding methods used as well as the detection performance were compared and evaluated through GRU-based anomaly detection model using vectors generated by the embedding model as an input. Compared to the statistical model, it was confirmed that the Skip-gram maintains more stable performance without biasing a specific window size or sequence length, and is more effective in making each event of sequence data into an embedding vector.

• Journal of the Korea Computer Industry Society
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• v.7 no.5
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• pp.487-494
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• 2006

By development of the superhigh speed network and the Networked appliance, a home network environment was equipped quickly around the cyber apartment. The home network environment provides the abundant family life style which numerous appliance and tools are connected with the network. Recently it is caused by with appearance of the Networked appliance which is connected with the network, the service demand is augmented to hacking, wrong operation, breakdown, crime prevention in home automation, fire, and break-in. This paper planned and proposed the integrated crisis management service model in the environment of home network and Networked appliance. Namely, it classifies the Networked appliance crisis management service and it defines a crisis management message with the XML. The message where the crisis situation occurs is notified and controlled in wireless PDA or the hand phone or the specific authorization.

• Convergence Security Journal
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• v.6 no.2
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• pp.31-42
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• 2006

In this paper, we proposed an integrated infrastructure for optimal information security to resolve these kinds of problems and to implement more powerful protection. The proposed infrastructure presents a security framework, provides a functional mechanism, and implements a scheme for information security based on the design concept of integrated structures. In order to ensure effective malicious traffic blocking, this paper emphasizes that a comprehensive approach through infrastructure improvement and combination of scanning tool is the only measure for preparing against today's environment of virus infiltration. The proposed model is a measure developed at a time when a permanent technological solution to virus is yet to be developed. A performance analysis model is developed and the performance is evaluated through the case studies for the proposed methodology. The effectiveness of the infrastructure for optimal information security needs the continuous diagnostic evaluation and tuning through the users or the organizations.

• Annual Conference of KIPS
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• 2005.11a
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• pp.19-22
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• 2005

본 논문에서는 대용량 네트워크 트래픽 데이터를 대상으로 사이트의 프라이버시를 보호하면서 마이닝 결과의 정확성, 실용성 등을 보장할 수 있는 효율적인 순차 패턴 마이닝 기법을 제안한다. 네트워크가 발달함에 따라 네트워크 트래픽 데이터에 대한 마이닝은 네트워크를 통한 통신의 패턴을 찾아내고, 이를 사용하여 침입 탐지, 인터넷 웜의 탐지 등으로 유용하게 쓰이게 되었다. 그러나 네트워크 트래픽 데이터는 네트워크 사용자 개개인의 인터넷 접속 형태, IP 주소 등의 정보를 포함하는 데이터로 네트워크 사용자의 프라이버시를 해칠 수 있다는 문제점이 존재한다. 따라서 이들 네트워크 트래픽 데이터를 대상으로 하는 마이닝 기법에서는 프라이버시 보호를 위하여 각 사이트에 저장되어 있는 네트워크 트래픽 데이터를 공개하지 않으면서도, 의미있는 패턴을 찾을 수 있어야 한다. 본 논문에서는 프라이버시 보호를 위하여 N-저장소 서버 모델을 제안한다. 제안된 모델에서는 데이터를 분할하여 암호화한 후, 이를 복호화할 수 없는 서버에서 집계하는 방식을 사용하여 실제 데이터가 저장되어 있는 각 사이트의 출처 정보를 감추는 방식을 사용한다. 또한, 효율적인 빈번 패턴 생성을 위하여 빈번 항목에 대한 인덱스 구조를 제안하고, 이를 기반으로 한 순차 패턴 마이닝 기법을 보인다.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.246-250
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• 2004

국내 수자원의 지역적 불균형으로 가뭄 시 해안 지역은 제한급수지역의 대부분을 차지하였다(건교부, 2001). 또한 해안지역의 평균 상수도 보급률은 $40\%$대로서 전국 평균 $87.1\%$(환경부, 2001)의 절반 정도이다. 실제로 해안지역의 지하수 이용량은 전국 지하수 이용량 약 31억$m^3$/년(수자원공사, 2002)의 약 $21\%$를 차지하고 있지만, 1인당 지하수 이용량은 전국 평균 $65m^3$의 4배에 달하는 것으로 조사된 바가 있다(홍성훈, 2003). 즉, 용수공급원의 부족으로 해안지역에서는 지하수에 대한 의존도가 높으며, 이로 인해 해안 지역에서 무분별한 지하수 개발과 그로 인한 해수침입 등의 환경 장애와 더불어 폐공 발생수가 증가하고 있는 실정이다. 따라서, 이런 문제점들과 해안지역을 고려한 지하수의 지속적 확보가 절실히 필요하다. 국외 인구동향을 보면 밀도류나 해수침투와 같은 해안지역 특성을 고려한 지하수 최적개발 모델이 개랄 또는 적용되어지고 있다. 하지만 해안지역 지하수 개랄 및 관리에서 요구하는 다양한 충족조건 내신 하나의 목적함수(예를 들어 최적 양수량, 최적 비용 등)만을 고려하고 있다. 그렇지만 실제적인 문제에서는 어느 위치에서 얼마만큼 개발되어야 하는지를 고려해야만 한다. 따라서 본 연구에서는 관정의 최적 개발량과 치적 위치라는 두개의 최적해를 고려할 수 있는 최적 양수모델을 제시하고, 실험실 수리모형에서의 검증을 소개하고자 한다. 또한 해안 지하수의 지속적 확보를 위한 방안을 제시하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.841-844
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• 2005

As various mobile technologies, sensor technologies, remote control and infrastructure are developing and expectations on quality of life are increasing, a lot of researches and developments on home network technologies and services are actively on going. Until now, we focused on how to provide users with high-level home network services, while not many researches on home network security for guaranteeing safety are progressing, So, in this paper, we propose an access control model for home network that provides various users with home network services up one's characteristics and features, and protects home network systems from illegal accesses or intrusions.

• Annual Conference of KIPS
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• 2022.11a
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• pp.1056-1058
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• 2022

본 연구는 항만에서 안전 수칙을 위반하여 발생하는 사고 및 이상행동을 실시간 탐지를 수행한 후 위험 상황을 관리자가 신속하고 정확하게 대처할 수 있도록 지원하는 지능형 CCTV, Smart Eye를 제안한다. Smart Eye는 컴퓨터 비전(Computer Vision) 기반의 다양한 객체 탐지(Object Detection) 모델과 행동 인식(Action Recognition) 모델을 통해 낙하 및 전도사고, 안전 수칙 미준수 인원, 폭력적인 행동을 보이는 인원을 복합적으로 판단하며, 객체 추적(Object Tracking), 관심 영역(Region of Interest), 객체 간의 거리 측정 알고리즘을 구현하여, 제한구역 접근, 침입, 배회, 안전 보호구 미착용 인원 그리고 화재 및 충돌사고 위험도를 측정한다. 해당 연구를 통한 자동화된 24시간 감시체계는 실시간 영상 데이터 분석 및 판단 처리 과정을 거친 후 각 장소에서 수집된 데이터를 관리자에게 신속히 전달하고 항만 내 통합관제센터에 접목함으로써 효율적인 관리 및 운영할 수 있게 하는 '지능형 인프라'를 구축할 수 있다. 이러한 체계는 곧 스마트 항만 시스템 도입에 이바지할 수 있을 것으로 기대된다.